A comprehensive survey of data classification based on evidence theory

Linqing HUANG; Jinfu FAN; Gongshen LIU; Alan Wee-Chung LIEW

doi:10.1631/ENG.ITEE.2026.0125

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A comprehensive survey of data classification based on evidence theory

Regular Papers | Updated：2026-10-08

- A comprehensive survey of data classification based on evidence theory
- A comprehensive survey of data classification based on evidence theory
- ENGINEERING Information Technology & Electronic Engineering Vol. 27, Issue 9, (2026)
- Affiliations：
  
  School of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China
  College of Computer Science and Technology, Qingdao University, Qingdao 266071, China
  School of Information and Communication Technology, Griffith University, Gold Coast, QLD 4215, Australia
- Author bio：
  
  ✉Jinfu FAN, fan_jinfu@163.com
- Funds：
- DOI：10.1631/ENG.ITEE.2026.0125
  CLC： TP181
- Received：28 April 2026，
  
  Revised：2026-06-05，
  
  Published：01 September 2026
- Accepted：
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Linqing HUANG, Jinfu FAN, Gongshen LIU, et al. A comprehensive survey of data classification based on evidence theory[J]. ENGINEERING Information Technology & Electronic Engineering, 2026, 27(9): 260125-30.
DOI：

Linqing HUANG, Jinfu FAN, Gongshen LIU, et al. A comprehensive survey of data classification based on evidence theory[J]. ENGINEERING Information Technology & Electronic Engineering, 2026, 27(9): 260125-30. DOI： 10.1631/ENG.ITEE.2026.0125.

摘要

Abstract

Data classification is a fundamental task in machine learning and data analysis

with applications across many fields. In practice

uncertainty often arises due to weakly discriminative features

missing values

distribution shift

class imbalance

inconsistent label spaces

and class overlap. Traditional classification methods

which typically rely on probabilistic frameworks

may not explicitly represent or reduce such uncertainty. Evidence theory (ET)

also known as Dempster–Shafer theory

provides a flexible framework for modeling uncertainty and imprecision through basic belief assignments and the evidence combination rule

and has attracted growing attention in the data classification field. Consequently

data classification based on ET (DCET) has become an active research topic. This paper provides a comprehensive survey of DCET

systematically categorizing DCET methods by feature- and label-based uncertainty scenarios. We first review ET fundamentals and summarize the evidential

-nearest-neighbor classifier and its variants. We then survey ET-based methods for tabular data classification under six major uncertainty scenarios: high-dimensional features

missing attribute values

feature distribution shift

imbalanced label distribution

inconsistent label spaces

and class over

lap. Next

we review ET-based methods for image (unstructured) classification

highlighting the integration of ET with deep neural networks. We further summarize representative applications

including human activity recognition

medical image segmentation

remote sensing image classification

and remote sensing image change detection. Finally

we discuss future research directions for DCET.

关键词

Keywords

references

Abdar M , Pourpanah F , Hussain S , et al. , 2021 . A review of uncertainty quantification in deep learning: techniques, applications and challenges . Inform Fus , 76 : 243 - 297 . https://doi.org/10.1016/j.inffus.2021.05.008 https://doi.org/10.1016/j.inffus.2021.05.008

Ahmad F , Xiong J , Wu J , et al. , 2025 . Cardiac dynamic characteristics classification on cine MRI using semi-supervised imaging approach . Mach Intell Res , 22 ( 6 ): 1102 - 1115 . https://doi.org/10.1007/s11633-024-1534-0 https://doi.org/10.1007/s11633-024-1534-0

Alcala-Fdez J , Alcala R , Herrera F , 2011 . A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning . IEEE Trans Fuzzy Syst , 19 ( 5 ): 857 - 872 . https://doi.org/10.1109/TFUZZ.2011.2147794 https://doi.org/10.1109/TFUZZ.2011.2147794

Amini A , Schwarting W , Soleimany A , et al. , 2020 . Deep evidential regression . Proc 34 th Int Conf on Neural Information Processing Systems , Article 1251 .

Attoh-Okine NO , 2002 . Aggregating evidence in pavement management decision-making using belief functions and qualitative Markov tree . IEEE Trans Syst Man Cybern Part C (Appl Rev) , 32 ( 3 ): 243 - 251 . https://doi.org/10.1109/tsmcc.2002.804443 https://doi.org/10.1109/tsmcc.2002.804443

Campagner A , Ciucci D , Denœux T , 2022 . Belief functions and rough sets: survey and new insights . Int J Approx Reason , 143 : 192 - 215 . https://doi.org/10.1016/j.ijar.2022.01.011 https://doi.org/10.1016/j.ijar.2022.01.011

Cao Y , Zhou ZJ , Hu CH , et al. , 2021 . On the interpretability of belief rule-based expert systems . IEEE Trans Fuzzy Syst , 29 ( 11 ): 3489 - 3503 . https://doi.org/10.1109/TFUZZ.2020.3024024 https://doi.org/10.1109/TFUZZ.2020.3024024

Chang LL , Zhou ZJ , Liao HC , et al. , 2019 . Generic disjunctive belief-rule-base modeling, inferencing, and optimization . IEEE Trans Fuzzy Syst , 27 ( 9 ): 1866 - 1880 . https://doi.org/10.1109/TFUZZ.2019.2892348 https://doi.org/10.1109/TFUZZ.2019.2892348

Chen L , Lou YH , He JZ , et al. , 2022 . Evidential neighborhood contrastive learning for universal domain adaptation . Proc 36 th AAAI Conf on Artificial Intelligence , p. 6258 - 6267 . https://doi.org/10.1609/aaai.v36i6.20575 https://doi.org/10.1609/aaai.v36i6.20575

Chen M , Goel K , Sohoni NS , et al. , 2021 . Mandoline: model evaluation under distribution shift . Proc 38 th Int Conf on Machine Learning , p. 1617 - 1629 .

Chen ZC , Cai R , 2022 . Updating incomplete framework of target recognition database based on fuzzy gap statistic . Eng Appl Artif Intell , 107 : 104521 . https://doi.org/10.1016/j.engappai.2021.104521 https://doi.org/10.1016/j.engappai.2021.104521

Cui HZ , Chang YH , Zhang HQ , et al. , 2023 . Determine the number of unknown targets in the open world from the perspective of bidirectional analysis using gap statistic and isolation forest . Inform Sci , 623 : 832 - 856 . https://doi.org/10.1016/j.ins.2022.12.034 https://doi.org/10.1016/j.ins.2022.12.034

Dai YW , Zhu HQ , Yang SY , et al. , 2022 . GCL-OSDA: uncertainty prediction-based graph collaborative learning for open-set domain adaptation . Knowl-Based Syst , 256 : 109850 . https://doi.org/10.1016/j.knosys.2022.109850 https://doi.org/10.1016/j.knosys.2022.109850

Deb R , Liew AWC , 2016 . Missing value imputation for the analysis of incomplete traffic accident data . Inform Sci , 339 : 274 - 289 . https://doi.org/10.1016/j.ins.2016.01.018 https://doi.org/10.1016/j.ins.2016.01.018

Dempster AP , 1967 . Upper and lower probabilities induced by a multivalued mapping . Ann Math Statist , 38 ( 2 ): 325 - 339 . https://doi.org/10.1214/aoms/1177698950 https://doi.org/10.1214/aoms/1177698950

Deng Z , Wang JY , 2021 . A new evidential similarity measurement based on Tanimoto measure and its application in multi-sensor data fusion . Eng Appl Artif Intell , 104 : 104380 . https://doi.org/10.1016/j.engappai.2021.104380 https://doi.org/10.1016/j.engappai.2021.104380

Denœux T , 1995 . A K -nearest neighbor classification rule based on Dempster-Shafer theory . IEEE Trans Syst Man Cybern , 25 ( 5 ): 804 - 813 . https://doi.org/10.1109/21.376493 https://doi.org/10.1109/21.376493

Denœux T , 1997 . Analysis of evidence-theoretic decision rules for pattern classification . Patt Recogn , 30 ( 7 ): 1095 - 1107 . https://doi.org/10.1016/S0031-3203(96)00137-9 https://doi.org/10.1016/S0031-3203(96)00137-9

Denœux T , 2000 . A neural network classifier based on Dempster-Shafer theory . IEEE Trans Syst Man Cybern Part A Syst Humans , 30 ( 2 ): 131 - 150 . https://doi.org/10.1109/3468.833094 https://doi.org/10.1109/3468.833094

Denœux T , 2008 . Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence . Artif Intell , 172 ( 2-3 ): 234 - 264 . https://doi.org/10.1016/j.artint.2007.05.008 https://doi.org/10.1016/j.artint.2007.05.008

Denœux T , 2016 . 40 years of Dempster–Shafer theory . Int J Approx Reason , 79 ( C ): 1 - 6 . https://doi.org/10.1016/j.ijar.2016.07.010 https://doi.org/10.1016/j.ijar.2016.07.010

Denœux T , 2019a . Decision-making with belief functions: a review . Int J Approx Reason , 109 : 87 - 110 . https://doi.org/10.1016/j.ijar.2019.03.009 https://doi.org/10.1016/j.ijar.2019.03.009

Denœux T , 2019b . Logistic regression, neural networks and Dempster–Shafer theory: a new perspective . Knowl-Based Syst , 176 : 54 - 67 . https://doi.org/10.1016/j.knosys.2019.03.030 https://doi.org/10.1016/j.knosys.2019.03.030

Denœux T , 2021 . Distributed combination of belief functions . Inform Fus , 65 : 179 - 191 . https://doi.org/10.1016/j.inffus.2020.09.001 https://doi.org/10.1016/j.inffus.2020.09.001

Denœux T , 2025 . Uncertainty measures in a generalized theory of evidence . Fuzzy Sets Syst , 520 : 109546 . https://doi.org/10.1016/j.fss.2025.109546 https://doi.org/10.1016/j.fss.2025.109546

Denœux T , Masson MH , 2004 . EVCLUS: evidential clustering of proximity data . IEEE Trans Syst Man Cybern Part B (Cybern) , 34 ( 1 ): 95 - 109 . https://doi.org/10.1109/tsmcb.2002.806496 https://doi.org/10.1109/tsmcb.2002.806496

Denœux T , Shenoy PP , 2020 . An interval-valued utility theory for decision making with Dempster-Shafer belief functions . Int J Approx Reason , 124 : 194 - 216 . https://doi.org/10.1016/j.ijar.2020.06.008 https://doi.org/10.1016/j.ijar.2020.06.008

Denœux T , Smets P , 2006 . Classification using belief functions: relationship between case-based and model-based approaches . IEEE Trans Syst Man Cybern Part B (Cybern) , 36 ( 6 ): 1395 - 1406 . https://doi.org/10.1109/tsmcb.2006.877795 https://doi.org/10.1109/tsmcb.2006.877795

Denœux T , Kanjanatarakul O , Sriboonchitta S , 2019 . A new evidential K -nearest neighbor rule based on contextual discounting with partially supervised learning . Int J Approx Reason , 113 : 287 - 302 . https://doi.org/10.1016/j.ijar.2019.07.009 https://doi.org/10.1016/j.ijar.2019.07.009

Dong GG , Kuang GY , 2015 . Target recognition via information aggregation through Dempster–Shafer’s evidence theory . IEEE Geosci Remote Sens Lett , 12 ( 6 ): 1247 - 1251 . https://doi.org/10.1109/LGRS.2015.2390914 https://doi.org/10.1109/LGRS.2015.2390914

Dong YL , Zhou Y , 2023 . Hierarchi cal belief K -nearest neighbors for human activity recognition . Proc 42 nd Chinese Control Conf , p. 3109 - 3114 . https://doi.org/10.23919/CCC58697.2023.10240988 https://doi.org/10.23919/CCC58697.2023.10240988

Dong YL , Li XD , Dezert J , et al. , 2020 . Dezert-Smarandache theory-based fusion for human activity recognition in body sensor networks . IEEE Trans Ind Inform , 16 ( 11 ): 7138 - 7149 . https://doi.org/10.1109/TII.2020.2976812 https://doi.org/10.1109/TII.2020.2976812

Dong YL , Li XD , Dezert J , et al. , 2021a . Evidential reasoning with hesitant fuzzy belief structures for human activity recognition . IEEE Trans Fuzzy Syst , 29 ( 12 ): 3607 - 3619 . https://doi.org/10.1109/TFUZZ.2021.3079495 https://doi.org/10.1109/TFUZZ.2021.3079495

Dong YL , Li XD , Dezert J , et al. , 2021b . Multi-criteria analysis of sensor reliability for wearable human activity recognition . IEEE Sens J , 21 ( 17 ): 19144 - 19156 . https://doi.org/10.1109/JSEN.2021.3089579 https://doi.org/10.1109/JSEN.2021.3089579

Dong YL , Zhou RG , Zhu CM , et al. , 2022 . Hierarchical activity recognition based on belief functions theory in body sensor networks . IEEE Sens J , 22 ( 15 ): 15211 - 15221 . https://doi.org/10.1109/JSEN.2022.3186086 https://doi.org/10.1109/JSEN.2022.3186086

Dong YL , Li XD , Dezert J , et al. , 2023a . Multisource weighted domain adaptation with evidential reasoning for activity recognition . IEEE Trans Ind Inform , 19 ( 4 ): 5530 - 5542 . https://doi.org/10.1109/tii.2022.3182780 https://doi.org/10.1109/tii.2022.3182780

Dong YL , Xu KH , Zhu CM , et al. , 2023b . E-FPN: evidential feature pyramid network for ship classification . Remote Sens , 15 ( 15 ): 3916 . https://doi.org/10.3390/rs15153916 https://doi.org/10.3390/rs15153916

Dong YL , Li XD , Dezert J , et al. , 2024 . Graph-structure-based multigranular belief fusion for human activity recognition . IEEE Trans Neur Netw Learn Syst , 35 ( 10 ): 13589 - 13603 . https://doi.org/10.1109/tnnls.2023.3270290 https://doi.org/10.1109/tnnls.2023.3270290

Dong YL , Shi ZL , Li XD , et al. , 2025 . Multiview uncertainty-aware fusion for human activity recognition via Dempster–Shafer theory . IEEE Trans Ind Inform , 21 ( 4 ): 3037 - 3046 . https://doi.org/10.1109/TII.2024.3523536 https://doi.org/10.1109/TII.2024.3523536

Du PJ , Liu SC , Gamba P , et al. , 2012 . Fusion of difference images for change detection over urban areas . IEEE J Sel Top Appl Earth Obs Remote Sens , 5 ( 4 ): 1076 - 1086 . https://doi.org/10.1109/JSTARS.2012.2200879 https://doi.org/10.1109/JSTARS.2012.2200879

Du PJ , Liu SC , Xia JS , et al. , 2013 . Information fusion techniques for change detection from multi-temporal remote sensing images . Inform Fus , 14 ( 1 ): 19 - 27 . https://doi.org/10.1016/j.inffus.2012.05.003 https://doi.org/10.1016/j.inffus.2012.05.003

Dubois D , Prade H , 1988 . Representation and combination of uncertainty with belief functions and possibility measures . Comput Intell , 4 ( 3 ): 244 - 264 . https://doi.org/10.1111/j.1467-8640.1988.tb00279.x https://doi.org/10.1111/j.1467-8640.1988.tb00279.x

Fan JF , Yu Y , Wang ZJ , et al. , 2022 . Partial label learning based on disambiguation correction net with graph representation . IEEE Trans Circ Syst Video Technol , 32 ( 8 ): 4953 - 4967 . https://doi.org/10.1109/TCSVT.2021.3139968 https://doi.org/10.1109/TCSVT.2021.3139968

Fan JF , Yu Y , Huang LQ , et al. , 2023 . GraphDPI: partial label disambiguation by graph representation learning via mutual information maximization . Patt Recogn , 134 : 109133 . https://doi.org/10.1016/j.patcog.2022.109133 https://doi.org/10.1016/j.patcog.2022.109133

Fan JF , Huang LQ , Gong CY , et al. , 2025 . KMT-PLL: K-means cross-attention Transformer for partial label learning . IEEE Trans Neur Netw Learn Syst , 36 ( 2 ): 2789 - 2800 . https://doi.org/10.1109/TNNLS.2023.3347792 https://doi.org/10.1109/TNNLS.2023.3347792

Fan JF , Li J , Zhong X , et al. , 2026 . Evidential deep partial label learning to quantify disambiguation uncertainty . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 24770 - 24779 .

Fei LG , Lu JD , Feng YQ , 2020 . An extended best-worst multi-criteria decision-making method by belief functions and its applications in hospital service evaluation . Comput Ind Eng , 142 : 106355 . https://doi.org/10.1016/j.cie.2020.106355 https://doi.org/10.1016/j.cie.2020.106355

Feng J , Huang LQ , Li JN , et al. , 2026 . Partial label learning via mutual information representation learning . IEEE Trans Circ Syst Video Technol , 36 ( 3 ): 3709 - 3723 . https://doi.org/10.1109/TCSVT.2025.3621307 https://doi.org/10.1109/TCSVT.2025.3621307

Feng YB , Yi X , Wang SH , et al. , 2025 . HyperEDL: spectral-spatial evidence deep learning for cross-scene hyperspectral image classification . IEEE Trans Geosci Remote Sens , 63 : 5508316 . https://doi.org/10.1109/TGRS.2025.3549419 https://doi.org/10.1109/TGRS.2025.3549419

Feng ZC , Zhou ZJ , Hu CH , et al. , 2019 . A new belief rule base model with attribute reliability . IEEE Trans Fuzzy Syst , 27 ( 5 ): 903 - 916 . https://doi.org/10.1109/TFUZZ.2018.2878196 https://doi.org/10.1109/TFUZZ.2018.2878196

Feng ZC , He W , Zhou ZJ , et al. , 2021 . A new safety assessment method based on belief rule base with attribute reliability . IEEE/CAA J Autom Sin , 8 ( 11 ): 1774 - 1785 . https://doi.org/10.1109/JAS.2020.1003399 https://doi.org/10.1109/JAS.2020.1003399

Frikha A , Moalla H , 2015 . Analytic hierarchy process for multi-sensor data fusion based on belief function theory . Eur J Oper Res , 241 ( 1 ): 133 - 147 . https://doi.org/10.1016/j.ejor.2014.08.024 https://doi.org/10.1016/j.ejor.2014.08.024

Fu C , Zhan QS , Liu WY , 2021 . Evidential reasoning based ensemble classifier for uncertain imbalanced data . Inform Sci , 578 : 378 - 400 . https://doi.org/10.1016/j.ins.2021.07.027 https://doi.org/10.1016/j.ins.2021.07.027

Fu YG , Ye JF , Yin ZF , et al. , 2021 . Construction of EBRB classifier for imbalanced data based on fuzzy c-means clustering . Knowl-Based Syst , 234 : 107590 . https://doi.org/10.1016/j.knosys.2021.107590 https://doi.org/10.1016/j.knosys.2021.107590

Fu YM , Liu ZG , Zhang ZW , 2023 . Progressive learning vision Transformer for open set recognition of fine-grained objects in remote sensing images . IEEE Trans Geosci Remote Sens , 61 : 5215113 . https://doi.org/10.1109/TGRS.2023.3309091 https://doi.org/10.1109/TGRS.2023.3309091

Fu YM , Liu ZG , Wang ZC , 2024 . Logit prototype learning with active multimodal representation for robust open-set recognition . Sci China Inform Sci , 67 ( 6 ): 162204 . https://doi.org/10.1007/s11432-023-3924-x https://doi.org/10.1007/s11432-023-3924-x

Gao JY , Chen MY , Xiang LY , et al. , 2026 . A comprehensive survey on evidential deep learning and its applications . IEEE Trans Patt Anal Mach Intell , 48 ( 3 ): 2118 - 2138 . https://doi.org/10.1109/TPAMI.2025.3625258 https://doi.org/10.1109/TPAMI.2025.3625258

Gao M , Hong X , Harris CJ , 2014 . Construction of neurofuzzy models for imbalanced data classification . IEEE Trans Fuzzy Syst , 22 ( 6 ): 1472 - 1488 . https://doi.org/10.1109/tfuzz.2013.2296091 https://doi.org/10.1109/tfuzz.2013.2296091

Geng CX , Huang SJ , Chen SC , 2021 . Recent advances in open set recognition: a survey . IEEE Trans Patt Anal Mach Intell , 43 ( 10 ): 3614 - 3631 . https://doi.org/10.1109/TPAMI.2020.2981604 https://doi.org/10.1109/TPAMI.2020.2981604

Geng XJ , Liang Y , Jiao LM , 2020 . Multi-frame decision fusion based on evidential association rule mining for target identification . Appl Soft Comput , 94 : 106460 . https://doi.org/10.1016/j.asoc.2020.106460 https://doi.org/10.1016/j.asoc.2020.106460

Geng XJ , Liang Y , Jiao LM , 2021 . Evidential association classification for high-dimensional data . Proc IEEE Int Conf on Cloud Computing and Big Data Analytics , p. 100 - 105 . https://doi.org/10.1109/ICCCBDA51879.2021.9442509 https://doi.org/10.1109/ICCCBDA51879.2021.9442509

Geng XJ , Chen XH , Ma ZF , et al. , 2024 . Multi-framework evidential association rule fusion for wearable human activity recognition . IEEE Sens J , 24 ( 7 ): 11805 - 11816 . https://doi.org/10.1109/JSEN.2024.3363908 https://doi.org/10.1109/JSEN.2024.3363908

Gil-Gonzalez J , Orozco-Gutierrez A , Alvarez-Meza A , 2021 . Learning from multiple inconsistent and dependent annotators to support classification tasks . Neurocomputing , 423 : 236 - 247 . https://doi.org/10.1016/j.neucom.2020.10.045 https://doi.org/10.1016/j.neucom.2020.10.045

Gong CY , Su ZG , Wang PH , et al. , 2020a . Cumulative belief peaks evidential K -nearest neighbor clustering . Knowl-Based Syst , 200 : 105982 . https://doi.org/10.1016/j.knosys.2020.105982 https://doi.org/10.1016/j.knosys.2020.105982

Gong CY , Wang PH , Su ZG , 2020b . An interactive nonparametric evidential regression algorithm with instance selection . Soft Comput , 24 ( 5 ): 3125 - 3140 . https://doi.org/10.1007/s00500-020-04667-4 https://doi.org/10.1007/s00500-020-04667-4

Gong CY , Su ZG , Wang PH , et al. , 2021a . Evidential instance selection for K -nearest neighbor classification of big data . Int J Approx Reason , 138 : 123 - 144 . https://doi.org/10.1016/j.ijar.2021.08.006 https://doi.org/10.1016/j.ijar.2021.08.006

Gong CY , Su ZG , Wang PH , et al. , 2021b . An evidential clustering algorithm by finding belief-peaks and disjoint neighborhoods . Patt Recogn , 113 : 107751 . https://doi.org/10.1016/j.patcog.2020.107751 https://doi.org/10.1016/j.patcog.2020.107751

Gong CY , Su ZG , Wang Q , et al. , 2022a . Distributed EK-NN classification . Proc 7 th Int Conf on Belief Functions: Theory and Applications , p. 99 - 108 . https://doi.org/10.1007/978-3-031-17801-6_10 https://doi.org/10.1007/978-3-031-17801-6_10

Gong CY , Su ZG , Wang PH , et al. , 2022b . Distributed evidential clustering toward time series with big data issue . Expert Syst Appl , 191 : 116279 . https://doi.org/10.1016/j.eswa.2021.116279 https://doi.org/10.1016/j.eswa.2021.116279

Gong CY , Li YB , Liu Y , et al. , 2022c . Joint evidential K -nearest neighbor classification . Proc IEEE 38 th Int Conf on Data Engineering , p. 2113 - 2126 . https://doi.org/10.1109/ICDE53745.2022.00204 https://doi.org/10.1109/ICDE53745.2022.00204

Gong CY , Su ZG , Wang PH , et al. , 2023 . A sparse reconstructive evidential K -nearest neighbor classifier for high-dimensional data . IEEE Trans Knowl Data Eng , 35 ( 6 ): 5563 - 5576 . https://doi.org/10.1109/TKDE.2022.3157346 https://doi.org/10.1109/TKDE.2022.3157346

Gong CY , Demmel J , You Y , 2024 . Scalable evidential K -nearest neighbor classification on big data . IEEE Trans Big Data , 10 ( 3 ): 226 - 237 . https://doi.org/10.1109/TBDATA.2023.3327220 https://doi.org/10.1109/TBDATA.2023.3327220

Gong CY , Su ZG , Denœux T , 2025 . Multi-view evidential K -NN classification . Inform Fus , 120 : 103113 . https://doi.org/10.1016/j.inffus.2025.103113 https://doi.org/10.1016/j.inffus.2025.103113

Grina F , Elouedi Z , Lefevre E , 2022 . Learning from imbalanced data using an evidential undersampling-based ensemble . Proc 15 th Int Conf on Scalable Uncertainty Management , p. 235 - 248 . https://doi.org/10.1007/978-3-031-18843-5_16 https://doi.org/10.1007/978-3-031-18843-5_16

Grina F , Elouedi Z , Lefevre E , 2023 . Re-sampling of multi-class imbalanced data using belief function theory and ensemble learning . Int J Approx Reason , 156 : 1 - 15 . https://doi.org/10.1016/j.ijar.2023.02.006 https://doi.org/10.1016/j.ijar.2023.02.006

Gu TX , Huang LQ , Liu GS , 2025 . GTRNet: graph topology-aware refinement network for user role classification in social networks . Proc IEEE Int Conf on Systems, Man, and Cybernetics , p. 3307 - 3312 . https://doi.org/10.1109/SMC58881.2025.11342554 https://doi.org/10.1109/SMC58881.2025.11342554

Guan H , Song CF , Zhang ZX , 2025 . Lidar-camera cooperative semantic segmentation . Mach Intell Res , 22 ( 5 ): 956 - 968 . https://doi.org/10.1007/s11633-024-1508-2 https://doi.org/10.1007/s11633-024-1508-2

Hadi W , Al-Radaideh QA , Alhawari S , 2018 . Integrating associative rule-based classification with Naive Bayes for text classification . Appl Soft Comput , 69 : 344 - 356 . https://doi.org/10.1016/j.asoc.2018.04.056 https://doi.org/10.1016/j.asoc.2018.04.056

Han ZB , Zhang CQ , Fu HZ , et al. , 2021 . Trusted multi-view classification . Proc 9 th Int Conf on Learning Representations , p. 1 - 11 .

Han ZB , Yang F , Huang JZ , et al. , 2022 . Multimodal dynamics: dynamical fusion for trustworthy multimodal classification . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 20675 - 20685 . https://doi.org/10.1109/CVPR52688.2022.02005 https://doi.org/10.1109/CVPR52688.2022.02005

Han ZB , Zhang CQ , Fu HZ , et al. , 2023 . Trusted multi-view classification with dynamic evidential fusion . IEEE Trans Patt Anal Mach Intell , 45 ( 2 ): 2551 - 2566 . https://doi.org/10.1109/TPAMI.2022.3171983 https://doi.org/10.1109/TPAMI.2022.3171983

He HF , Liao RB , Li YT , 2025 . MSAFNet: a novel approach to facial expression recognition in embodied AI systems . Intell Robot , 5 ( 2 ): 313 - 332 . https://doi.org/10.20517/ir.2025.16 https://doi.org/10.20517/ir.2025.16

He WC , Jiang Z , Xiao TS , et al. , 2026 . A survey on uncertainty quantification methods for deep learning . ACM Comput Surv , 58 ( 7 ): 179 . https://doi.org/10.1145/3786319 https://doi.org/10.1145/3786319

He YL , Ye X , Huang JZ , et al. , 2022 . Bayesian attribute bagging-based extreme learning machine for high-dimensional classification and regression . ACM Trans Intell Syst Technol , 13 ( 2 ): 1 - 26 . https://doi.org/10.1145/3495164 https://doi.org/10.1145/3495164

Hu JW , Zheng BY , Wang C , et al. , 2020 . A survey on multi-sensor fusion based obstacle detection for intelligent ground vehicles in off-road environments . Front Inform Technol Electron Eng , 21 ( 5 ): 675 - 692 . https://doi.org/10.1631/FITEE.1900518 https://doi.org/10.1631/FITEE.1900518

Hu ZT , Su YJ , Hou W , et al. , 2023 . Multi-sensor data fusion method based on divergence measure and probability transformation belief factor . Appl Soft Comput , 145 : 110603 . https://doi.org/10.1016/j.asoc.2023.110603 https://doi.org/10.1016/j.asoc.2023.110603

Huang L , Denœux T , Tonnelet D , et al. , 2021a . Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation . Proc 12 th Int Workshop on Machine Learning in Medical Imaging , p. 30 - 39 . https://doi.org/10.1007/978-3-030-87589-3_4 https://doi.org/10.1007/978-3-030-87589-3_4

Huang L , Ruan S , Decazes P , et al. , 2021b . Evidential segmentation of 3D PET/CT images . Proc 6 th Int Conf on Belief Functions: Theory and Applications , p. 159 - 167 . https://doi.org/10.1007/978-3-030-88601-1_16 https://doi.org/10.1007/978-3-030-88601-1_16

Huang L , Denœux T , Vera P , et al. , 2022a . Evidence fusion with contextual discounting for multi-modality medical image segmentation . Proc 25 th Int Conf on Medical Image Computing and Computer Assisted Intervention , p. 401 - 411 . https://doi.org/10.1007/978-3-031-16443-9_39 https://doi.org/10.1007/978-3-031-16443-9_39

Huang L , Ruan S , Decazes P , et al. , 2022b . Lymphoma segmentation from 3D PET-CT images using a deep evidential network . Int J Approx Reason , 149 : 39 - 60 . https://doi.org/10.1016/j.ijar.2022.06.007 https://doi.org/10.1016/j.ijar.2022.06.007

Huang L , Ruan S , Denœux T , 2023a . Application of belief functions to medical image segmentation: a review . Inform Fus , 91 : 737 - 756 . https://doi.org/10.1016/j.inffus.2022.11.008 https://doi.org/10.1016/j.inffus.2022.11.008

Huang L , Liu ZG , Dezert J , 2023b . Cross-domain pattern classification with distribution adaptation based on evidence theory . IEEE Trans Cybern , 53 ( 2 ): 718 - 731 . https://doi.org/10.1109/TCYB.2021.3133890 https://doi.org/10.1109/TCYB.2021.3133890

Huang L , Ruan S , Denœux T , 2023c . Semi-supervised multiple evidence fusion for brain tumor segmentation . Neurocomputing , 535 : 40 - 52 . https://doi.org/10.1016/j.neucom.2023.02.047 https://doi.org/10.1016/j.neucom.2023.02.047

Huang LQ , 2024 . Combination of information in labeled and unlabeled data via evidence theory . IEEE Trans Artif Intell , 5 ( 5 ): 2179 - 2192 . https://doi.org/10.1109/TAI.2023.3316194 https://doi.org/10.1109/TAI.2023.3316194

Huang LQ , Liu GS , 2024 . Convolutional feature aggregation network with self-supervised learning and decision fusion for SAR target recognition . IEEE Trans Instrum Meas , 73 : 2526714 . https://doi.org/10.1109/TIM.2024.3443349 https://doi.org/10.1109/TIM.2024.3443349

Huang LQ , Liu GS , 2026 . Dual joint covariance alignment method for incomplete data classification . IEEE Trans Neur Netw Learn Syst , early access . https://doi.org/10.1109/TNNLS.2026.3707961 https://doi.org/10.1109/TNNLS.2026.3707961

Huang LQ , Liu ZG , Pan Q , et al. , 2020 . Evidential combination of augmented multi-source of information based on domain adaptation . Sci China Inform Sci , 63 ( 11 ): 210203 . https://doi.org/10.1007/s11432-020-3080-3 https://doi.org/10.1007/s11432-020-3080-3

Huang LQ , Fan JF , Zhao WB , et al. , 2023a . A new multi-source transfer learning method based on two-stage weighted fusion . Knowl-Based Syst , 262 : 110233 . https://doi.org/10.1016/j.knosys.2022.110233 https://doi.org/10.1016/j.knosys.2022.110233

Huang LQ , Zhao WB , Liew AWC , et al. , 2023b . An evidential combination method with multi-color spaces for remote sensing image scene classification . Inform Fus , 93 : 209 - 226 . https://doi.org/10.1016/j.inffus.2022.12.025 https://doi.org/10.1016/j.inffus.2022.12.025

Huang LQ , Fan JF , Xu K , 2024a . SAR target recognition based on convolutional feature aggregation and decision combination . Proc IEEE Int Geoscience and Remote Sensing Symp , p. 11008 - 11011 . https://doi.org/10.1109/IGARSS53475.2024.10641715 https://doi.org/10.1109/IGARSS53475.2024.10641715

Huang LQ , Zhao WB , Liu Y , et al. , 2024b . An evidential multi-target domain adaptation method based on weighted fusion for cross-domain pattern classification . IEEE Trans Neur Netw Learn Syst , 35 ( 10 ): 14218 - 14232 . https://doi.org/10.1109/TNNLS.2023.3275759 https://doi.org/10.1109/TNNLS.2023.3275759

Huang LQ , Chen BG , Hu YM , et al. , 2025a . Handcrafted and deep feature aggregation network for identification of real and fake satellite image with limited data . Proc IEEE Int Geoscience and Remote Sensing Symp , p. 6606 - 6609 . https://doi.org/10.1109/IGARSS55030.2025.11242735 https://doi.org/10.1109/IGARSS55030.2025.11242735

Huang LQ , Fan JF , Liew AWC , 2025b . Integration of multikinds imputation with covariance adaptation based on evidence theory . IEEE Trans Neur Netw Learn Syst , 36 ( 5 ): 8657 - 8671 . https://doi.org/10.1109/TNNLS.2024.3412371 https://doi.org/10.1109/TNNLS.2024.3412371

Huang LQ , Fan JF , Wang SL , et al. , 2025c . Multi-source multi-target domain adaptation based on evidence theory . IEEE Trans Instrum Meas , 74 : 2524317 . https://doi.org/10.1109/TIM.2025.3557120 https://doi.org/10.1109/TIM.2025.3557120

Huang LQ , Fan JF , Wang SL , et al. , 2025d . A new multi-target domain adaptation method based on evidence theory for distribution inconsistent data classification . IEEE Trans Syst Man Cybern Syst , 55 ( 6 ): 4125 - 4139 . https://doi.org/10.1109/tsmc.2025.3548988 https://doi.org/10.1109/tsmc.2025.3548988

Huang LQ , Fan JF , Wang SL , et al. , 2025e . Towards a distribution alignment framework for incomplete data classification . Proc IEEE Int Conf on Acoustics, Speech and Signal Processing , p. 1 - 5 . https://doi.org/10.1109/ICASSP49660.2025.10890244 https://doi.org/10.1109/ICASSP49660.2025.10890244

Huang LQ , Zhu J , Hu YM , et al. , 2025f . New multi-source distributed transfer learning framework . Proc IEEE Int Conf on Image Processing , p. 241 - 246 . https://doi.org/10.1109/ICIP55913.2025.11084639 https://doi.org/10.1109/ICIP55913.2025.11084639

Huang LQ , Fan JF , Liu GS , et al. , 2026a . A robust multi-source transfer classification method based on belief functions for cross-domain pattern recognition . Int J Approx Reason , 189 : 109589 . https://doi.org/10.1016/j.ijar.2025.109589 https://doi.org/10.1016/j.ijar.2025.109589

Huang LQ , Fan JF , Wang SL , et al. , 2026b . Incomplete data classification via distribution alignment with evidence combination . Mach Intell Res , 23 ( 3 ): 593 - 615 . https://doi.org/10.1007/s11633-025-1565-1 https://doi.org/10.1007/s11633-025-1565-1

Huang M , Zhang C , 2022 . A novel multi-source domain adaptation method with Dempster–Shafer evidence theory for cross-domain classification . Mathematics , 10 ( 15 ): 2797 . https://doi.org/10.3390/math10152797 https://doi.org/10.3390/math10152797

Huang T , Liu ZK , Wang R , et al. , 2025 . Visual hallucination detection in large vision-language models via evidential conflict . Int J Approx Reason , 186 : 109507 . https://doi.org/10.1016/j.ijar.2025.109507 https://doi.org/10.1016/j.ijar.2025.109507

Ji FC , Zhao WZ , Wang Q , et al. , 2024 . Spectral-spatial evidential learning network for open-set hyperspectral image classification . IEEE Trans Geosci Remote Sens , 62 : 5503617 . https://doi.org/10.1109/TGRS.2024.3349415 https://doi.org/10.1109/TGRS.2024.3349415

Jiang W , Chang Y , Wang SY , et al. , 2017 . A method to identify the incomplete framework of discernment in evidence theory . Math Probl Eng , 2017 : 7635972 . https://doi.org/10.1155/2017/7635972 https://doi.org/10.1155/2017/7635972

Jiao LM , Pan Q , Denœux T , et al. , 2015a . Belief rule-based classification system: extension of FRBCS in belief functions framework . Inform Sci , 309 : 26 - 49 . https://doi.org/10.1016/j.ins.2015.03.005 https://doi.org/10.1016/j.ins.2015.03.005

Jiao LM , Pan Q , Feng XX , 2015b . Multi-hypothesis nearest-neighbor classifier based on class-conditional weighted distance metric . Neurocomputing , 151 : 1468 - 1476 . https://doi.org/10.1016/j.neucom.2014.10.039 https://doi.org/10.1016/j.neucom.2014.10.039

Jiao LM , Denœux T , Pan Q , 2016 . A hybrid belief rule-based classification system based on uncertain training data and expert knowledge . IEEE Trans Syst Man Cybern Syst , 46 ( 12 ): 1711 - 1723 . https://doi.org/10.1109/tsmc.2015.2503381 https://doi.org/10.1109/tsmc.2015.2503381

Jiao LM , Geng XJ , Pan Q , 2019 . EE k NN: k -nearest neighbor classifier with an evidential editing procedure for training samples . Electronics , 8 ( 5 ): 592 . https://doi.org/10.3390/electronics8050592 https://doi.org/10.3390/electronics8050592

Jiao LM , Ma XG , Zhang H , et al. , 2026a . Dynamic belief rule learning for classification with expert in the loop . IEEE Trans Artif Intell , 7 ( 4 ): 2142 - 2156 . https://doi.org/10.1109/TAI.2025.3612317 https://doi.org/10.1109/TAI.2025.3612317

Jiao LM , Xiao XQ , Gou SJ , et al. , 2026b . An evidential deep neural network for set-valued classification and novelty detection . IEEE Trans Artif Intell , 7 ( 7 ): 4161 - 4175 . https://doi.org/10.1109/TAI.2026.3660627 https://doi.org/10.1109/TAI.2026.3660627

Jiao LM , Xiao XQ , Gou SJ , 2026c . MBHF-net: a multi-source belief hybrid fusion network for trusted target recognition . Neurocomputing , 669 : 132462 . https://doi.org/10.1016/j.neucom.2025.132462 https://doi.org/10.1016/j.neucom.2025.132462

Ju HR , Ding WP , Shi ZQ , et al. , 2022 . Attribute reduction with personalized information granularity of nearest mutual neighbors . Inform Sci , 613 : 114 - 138 . https://doi.org/10.1016/j.ins.2022.09.006 https://doi.org/10.1016/j.ins.2022.09.006

Ju HR , Lu Y , Ding WP , et al. , 2024 . Three-way evidence theory-based density peak clustering with the principle of justifiable granularity . Appl Soft Comput , 152 : 111217 . https://doi.org/10.1016/j.asoc.2023.111217 https://doi.org/10.1016/j.asoc.2023.111217

Kadir ME , Akash PS , Sharmin S , et al. , 2020 . A proximity weighted evidential k nearest neighbor classifier for imbalanced data . Proc 24 th Pacific-Asia Conf on Advances in Knowledge Discovery and Data Mining , p. 71 - 83 . https://doi.org/10.1007/978-3-030-47436-2_6 https://doi.org/10.1007/978-3-030-47436-2_6

Keller JM , Gray MR , Givens JA , 1985 . A fuzzy K -nearest neighbor algorithm . IEEE Trans Syst Man Cybern , SMC-15 ( 4 ): 580 - 585 . https://doi.org/10.1109/TSMC.1985.6313426 https://doi.org/10.1109/TSMC.1985.6313426

Koziarski M , 2020 . Radial-based undersampling for imbalanced data classification . Patt Recogn , 102 : 107262 . https://doi.org/10.1016/j.patcog.2020.107262 https://doi.org/10.1016/j.patcog.2020.107262

Laanaya H , Martin A , Aboutajdine D , et al. , 2010 . Support vector regression of membership functions and belief functions—application for pattern recognition . Inform Fus , 11 ( 4 ): 338 - 350 . https://doi.org/10.1016/j.inffus.2009.12.007 https://doi.org/10.1016/j.inffus.2009.12.007

Laghmara H , Laurain T , Cudel C , et al. , 2020 . Heterogeneous sensor data fusion for multiple object association using belief functions . Inform Fus , 57 : 44 - 58 . https://doi.org/10.1016/j.inffus.2019.11.002 https://doi.org/10.1016/j.inffus.2019.11.002

Laha A , Pal NR , Das J , 2006 . Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory . IEEE Trans Geosci Remote Sens , 44 ( 6 ): 1633 - 1641 . https://doi.org/10.1109/TGRS.2006.864391 https://doi.org/10.1109/TGRS.2006.864391

Le Hégarat-Mascle S , Seltz R , 2004 . Automatic change detection by evidential fusion of change indices . Remote Sens Environ , 91 ( 3-4 ): 390 - 404 . https://doi.org/10.1016/j.rse.2004.04.001 https://doi.org/10.1016/j.rse.2004.04.001

Le Hegarat-Mascle S , Bloch I , Vidal-Madjar D , 1997 . Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing . IEEE Trans Geosci Remote Sens , 35 ( 4 ): 1018 - 1031 . https://doi.org/10.1109/36.602544 https://doi.org/10.1109/36.602544

Lee H , Kwon H , 2021 . DBF: dynamic belief fusion for combining multiple object detectors . IEEE Trans Patt Anal Mach Intell , 43 ( 5 ): 1499 - 1514 . https://doi.org/10.1109/TPAMI.2019.2952847 https://doi.org/10.1109/TPAMI.2019.2952847

Lee H , Kwon H , Robinson RM , et al. , 2016 . Dynamic belief fusion for object detection . Proc IEEE Winter Conf on Applications of Computer Vision , p. 1 - 9 . https://doi.org/10.1109/WACV.2016.7477574 https://doi.org/10.1109/WACV.2016.7477574

Lelandais B , Ruan S , Denœux T , et al. , 2014 . Fusion of multi-tracer PET images for dose painting . Med Image Anal , 18 ( 7 ): 1247 - 1259 . https://doi.org/10.1016/j.media.2014.06.014 https://doi.org/10.1016/j.media.2014.06.014

Li BL , Han ZB , Li HN , et al. , 2022 . Trustworthy long-tailed classification . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 6960 - 6969 . https://doi.org/10.1109/CVPR52688.2022.00684 https://doi.org/10.1109/CVPR52688.2022.00684

Li XD , Dunkin F , Dezert J , 2024 . Multi-source information fusion: progress and future . Chin J Aeronaut , 37 ( 7 ): 24 - 58 . https://doi.org/10.1016/j.cja.2023.12.009 https://doi.org/10.1016/j.cja.2023.12.009

Li ZY , Huang LQ , Gu TH , et al. , 2025 . Partial multi-label learning via K-means graph Transformer . Knowl-Based Syst , 325 : 114017 . https://doi.org/10.1016/j.knosys.2025.114017 https://doi.org/10.1016/j.knosys.2025.114017

Lian CF , Ruan S , Denœux T , 2015 . An evidential classifier based on feature selection and two-step classification strategy . Patt Recogn , 48 ( 7 ): 2318 - 2327 . https://doi.org/10.1016/j.patcog.2015.01.019 https://doi.org/10.1016/j.patcog.2015.01.019

Lian CF , Ruan S , Denœux T , et al. , 2016a . Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction . Med Image Anal , 32 : 257 - 268 . https://doi.org/10.1016/j.media.2016.05.007 https://doi.org/10.1016/j.media.2016.05.007

Lian CF , Ruan S , Denœux T , 2016b . Dissimilarity metric learning in the belief function framework . IEEE Trans Fuzzy Syst , 24 ( 6 ): 1555 - 1564 . https://doi.org/10.1109/TFUZZ.2016.2540068 https://doi.org/10.1109/TFUZZ.2016.2540068

Lian CF , Ruan S , Denœux T , et al. , 2018 . Spatial evidential clustering with adaptive distance metric for tumor segmentation in FDG-PET images . IEEE Trans Biomed Eng , 65 ( 1 ): 21 - 30 . https://doi.org/10.1109/TBME.2017.2688453 https://doi.org/10.1109/TBME.2017.2688453

Lian CF , Ruan S , Denœux T , et al. , 2019 . Joint tumor segmentation in PET-CT images using co-clustering and fusion based on belief functions . IEEE Trans Image Process , 28 ( 2 ): 755 - 766 . https://doi.org/10.1109/TIP.2018.2872908 https://doi.org/10.1109/TIP.2018.2872908

Liew AWC , Law NF , Yan H , 2011 . Missing value imputation for gene expression data: computational techniques to recover missing data from available information . Brief Bioinform , 12 ( 5 ): 498 - 513 . https://doi.org/10.1093/bib/bbq080 https://doi.org/10.1093/bib/bbq080

Lin CJ , Leony F , 2024 . Evidence-based adaptive oversampling algorithm for imbalanced classification . Knowl Inform Syst , 66 ( 3 ): 2209 - 2233 . https://doi.org/10.1007/s10115-023-01985-5 https://doi.org/10.1007/s10115-023-01985-5

Liu C , Su ZG , Zhao G , 2022 . Evidential regression by synthesizing feature selection and parameters learning . Proc 7 th Int Conf on Belief Functions: Theory and Applications , p. 87 - 95 . https://doi.org/10.1007/978-3-031-17801-6_9 https://doi.org/10.1007/978-3-031-17801-6_9

Liu D , Zhang YR , Ren Y , et al. , 2025 . Multi-directional attention: a lightweight attention module for slender structures . Intell Robot , 5 ( 4 ): 827 - 843 . https://doi.org/10.20517/ir.2025.42 https://doi.org/10.20517/ir.2025.42

Liu F , Deng Y , 2021 . Determine the number of unknown targets in open world based on elbow method . IEEE Trans Fuzzy Syst , 29 ( 5 ): 986 - 995 .

Liu W , Yue XD , Chen YF , et al. , 2022 . Trusted multi-view deep learning with opinion aggregation . Proc 36 th AAAI Conf on Artificial Intelligence , p. 7585 - 7593 . https://doi.org/10.1609/aaai.v36i7.20724 https://doi.org/10.1609/aaai.v36i7.20724

Liu W , Chen YF , Yue XD , et al. , 2023 . Safe multi-view deep classification . Proc 37 th AAAI Conf on Artificial Intelligence , p. 8870 - 8878 . https://doi.org/10.1609/aaai.v37i7.26066 https://doi.org/10.1609/aaai.v37i7.26066

Liu YW , Zhang Y , Wang XD , et al. , 2025 . Evidential K -nearest neighbors with cognitive-inspired feature selection for high-dimensional data . Big Data Cogn Comput , 9 ( 8 ): 202 . https://doi.org/10.3390/bdcc9080202 https://doi.org/10.3390/bdcc9080202

Liu Z , 2023 . An effective conflict management method based on belief similarity measure and entropy for multi-sensor data fusion . Artif Intell Rev , 56 ( 12 ): 15495 - 15522 . https://doi.org/10.1007/s10462-023-10533-0 https://doi.org/10.1007/s10462-023-10533-0

Liu Z , Letchmunan S , 2024 . Representing uncertainty and imprecision in machine learning: a survey on belief functions . J King Saud Univ-Comput Inform Sci , 36 ( 1 ): 101904 . https://doi.org/10.1016/j.jksuci.2023.101904 https://doi.org/10.1016/j.jksuci.2023.101904

Liu ZG , Dezert J , Mercier G , et al. , 2012 . Dynamic evidential reasoning for change detection in remote sensing images . IEEE Trans Geosci Remote Sens , 50 ( 5 ): 1955 - 1967 .

Liu ZG , Pan Q , Dezert J , 2013a . Evidential classifier for imprecise data based on belief functions . Knowl-Based Syst , 52 : 246 - 257 . https://doi.org/10.1016/j.knosys.2013.08.005 https://doi.org/10.1016/j.knosys.2013.08.005

Liu ZG , Pan Q , Dezert J , 2013b . A new belief-based K -nearest neighbor classification method . Patt Recogn , 46 ( 3 ): 834 - 844 .

Liu ZG , Pan Q , Dezert J , 2014a . A belief classification rule for imprecise data . Appl Intell , 40 : 214 - 228 . https://doi.org/10.1007/s10489-013-0453-5 https://doi.org/10.1007/s10489-013-0453-5

Liu ZG , Pan Q , Dezert J , et al. , 2014b . Credal classification rule for uncertain data based on belief functions . Patt Recogn , 47 ( 7 ): 2532 - 2541 . https://doi.org/10.1016/j.patcog.2014.01.011 https://doi.org/10.1016/j.patcog.2014.01.011

Liu ZG , Mercier G , Dezert J , et al. , 2014c . Change detection in heterogeneous remote sensing images based on multidimensional evidential reasoning . IEEE Geosci Remote Sens Lett , 11 ( 1 ): 168 - 172 . https://doi.org/10.1109/lgrs.2013.2250908 https://doi.org/10.1109/lgrs.2013.2250908

Liu ZG , Pan Q , Dezert J , 2014d . Classification of uncertain and imprecise data based on evidence theory . Neurocomputing , 133 : 459 - 470 . https://doi.org/10.1016/j.neucom.2013.12.009 https://doi.org/10.1016/j.neucom.2013.12.009

Liu ZG , Liu Y , Dezert J , et al. , 2015a . Classification of incomplete data based on belief functions and K -nearest neighbors . Knowl-Based Syst , 89 : 113 - 125 . https://doi.org/10.1016/j.knosys.2015.06.022 https://doi.org/10.1016/j.knosys.2015.06.022

Liu ZG , Pan Q , Mercier G , et al. , 2015b . A new incomplete pattern classification method based on evidential reasoning . IEEE Trans Cybern , 45 ( 4 ): 635 - 646 . https://doi.org/10.1109/TCYB.2014.2332037 https://doi.org/10.1109/TCYB.2014.2332037

Liu ZG , Pan Q , Dezert J , et al. , 2016 . Adaptive imputation of missing values for incomplete pattern classification . Patt Recogn , 52 : 85 - 95 . https://doi.org/10.1016/j.patcog.2015.10.001 https://doi.org/10.1016/j.patcog.2015.10.001

Liu ZG , Pan Q , Dezert J , et al. , 2017 . Hybrid classification system for uncertain data . IEEE Trans Syst Man Cybern Syst , 47 ( 10 ): 2783 - 2790 . https://doi.org/10.1109/TSMC.2016.2622247 https://doi.org/10.1109/TSMC.2016.2622247

Liu ZG , Li G , Mercier G , et al. , 2018 . Change detection in heterogenous remote sensing images via homogeneous pixel transformation . IEEE Trans Image Process , 27 ( 4 ): 1822 - 1834 . https://doi.org/10.1109/TIP.2017.2784560 https://doi.org/10.1109/TIP.2017.2784560

Liu ZG , Huang LQ , Zhou K , et al. , 2021a . Combination of transferable classification with multisource domain adaptation based on evidential reasoning . IEEE Trans Neur Netw Learn Syst , 32 ( 5 ): 2015 - 2029 . https://doi.org/10.1109/TNNLS.2020.2995862 https://doi.org/10.1109/TNNLS.2020.2995862

Liu ZG , Zhang XX , Niu JW , et al. , 2021b . Combination of classifiers with different frames of discernment based on belief functions . IEEE Trans Fuzzy Syst , 29 ( 7 ): 1764 - 1774 . https://doi.org/10.1109/TFUZZ.2020.2985332 https://doi.org/10.1109/TFUZZ.2020.2985332

Liu ZG , Qiu GH , Mercier G , et al. , 2021c . A transfer classification method for heterogeneous data based on evidence theory . IEEE Trans Syst Man Cybern Syst , 51 ( 8 ): 5129 - 5141 . https://doi.org/10.1109/TSMC.2019.2945808 https://doi.org/10.1109/TSMC.2019.2945808

Liu ZG , Duan JF , Huang LQ , et al. , 2022a . Combination of classifiers with incomplete frames of discernment . Chin J Aeronaut , 35 ( 5 ): 145 - 157 . https://doi.org/10.1016/j.cja.2021.04.020 https://doi.org/10.1016/j.cja.2021.04.020

Liu ZG , Qiu GH , Wang SY , et al. , 2022b . A new belief-based bidirectional transfer classification method . IEEE Trans Cybern , 52 ( 8 ): 8101 - 8113 . https://doi.org/10.1109/TCYB.2021.3052536 https://doi.org/10.1109/TCYB.2021.3052536

Luo H , Liu C , Wu C , et al. , 2018 . Urban change detection based on Dempster–Shafer theory for multitemporal very high-resolution imagery . Remote Sens , 10 ( 7 ): 980 . https://doi.org/10.3390/rs10070980 https://doi.org/10.3390/rs10070980

Lv Y , Zhang BF , Zou GB , et al. , 2022 . Domain adaptation with data uncertainty measure based on evidence theory . Entropy , 24 ( 7 ): 966 . https://doi.org/10.3390/e24070966 https://doi.org/10.3390/e24070966

Lv Y , Ma JP , Zhang YQ , et al. , 2023 . Integrating priors into domain adaptation based on evidence theory . Proc 32 nd ACM Int Conf on Information and Knowledge Management , p. 1726 - 1735 . https://doi.org/10.1145/3583780.3614935 https://doi.org/10.1145/3583780.3614935

Ma CX , Liang Y , Zhang HX , et al. , 2024 . Distributed credible evidence fusion with privacy-preserving . Inform Fus , 112 : 102571 . https://doi.org/10.1016/j.inffus.2024.102571 https://doi.org/10.1016/j.inffus.2024.102571

Ma LY , Denœux T , 2021 . Partial classification in the belief function framework . Knowl-Based Syst , 214 : 106742 . https://doi.org/10.1016/j.knosys.2021.106742 https://doi.org/10.1016/j.knosys.2021.106742

Masson MH , Denœux T , 2008 . ECM: an evidential version of the fuzzy c-means algorithm . Patt Recogn , 41 ( 4 ): 1384 - 1397 . https://doi.org/10.1016/j.patcog.2007.08.014 https://doi.org/10.1016/j.patcog.2007.08.014

Meinert N , Gawlikowski J , Lavin A , 2023 . The unreasonable effectiveness of deep evidential regression . Proc 37 th AAAI Conf on Artificial Intelligence , p. 9134 - 9142 . https://doi.org/10.1609/aaai.v37i8.26096 https://doi.org/10.1609/aaai.v37i8.26096

Mullick SS , Datta S , Dhekane SG , et al. , 2020 . Appropriateness of performance indices for imbalanced data classification: an analysis . Patt Recogn , 102 : 107197 . https://doi.org/10.1016/j.patcog.2020.107197 https://doi.org/10.1016/j.patcog.2020.107197

Niu JW , Liu ZG , Lu Y , et al. , 2022 . Evidential combination of classifiers for imbalanced data . IEEE Trans Syst Man Cybern Syst , 52 ( 12 ): 7642 - 7653 . https://doi.org/10.1109/tsmc.2022.3162258 https://doi.org/10.1109/tsmc.2022.3162258

Niu ST , Liu YX , Wang J , et al. , 2020 . A decade survey of transfer learning (2010–2020) . IEEE Trans Artif Intell , 1 ( 2 ): 151 - 166 . https://doi.org/10.1109/TAI.2021.3054609 https://doi.org/10.1109/TAI.2021.3054609

Pan QT , Li ZR , Yang G , et al. , 2026 . EviVLM: when evidential learning meets vision language model for medical image segmentation . IEEE Trans Med Imag , 45 ( 4 ): 1369 - 1382 . https://doi.org/10.1109/TMI.2025.3622492 https://doi.org/10.1109/TMI.2025.3622492

Petit-Renaud S , Denœux T , 2004 . Nonparametric regression analysis of uncertain and imprecise data using belief functions . Int J Approx Reason , 35 ( 1 ): 1 - 28 . https://doi.org/10.1016/s0888-613x(03)00056-2 https://doi.org/10.1016/s0888-613x(03)00056-2

Qiao SH , Song BJ , Fan YS , et al. , 2023 . A fuzzy Dempster–Shafer evidence theory method with belief divergence for unmanned surface vehicle multi-sensor data fusion . J Mar Sci Eng , 11 ( 8 ): 1596 . https://doi.org/10.3390/jmse11081596 https://doi.org/10.3390/jmse11081596

Ramel S , Pichon F , Delmotte F , 2020 . A reliable version of choquistic regression based on evidence theory . Knowl-Based Syst , 205 : 106252 . https://doi.org/10.1016/j.knosys.2020.106252 https://doi.org/10.1016/j.knosys.2020.106252

Ribeiro MX , Traina AJM , Traina C , et al. , 2008 . An association rule-based method to support medical image diagnosis with efficiency . IEEE Trans Multim , 10 ( 2 ): 277 - 285 . https://doi.org/10.1109/TMM.2007.911837 https://doi.org/10.1109/TMM.2007.911837

Scheirer WJ , Jain LP , Boult TE , 2014 . Probability models for open set recognition . IEEE Trans Patt Anal Mach Intell , 36 ( 11 ): 2317 - 2324 . https://doi.org/10.1109/TPAMI.2014.2321392 https://doi.org/10.1109/TPAMI.2014.2321392

Sebbak F , Chibani A , Amirat Y , et al. , 2012 . An evidential fusion approach for activity recognition under uncertainty in ambient intelligence environments . Proc ACM Conf on Ubiquitous Computing , p. 834 - 840 . https://doi.org/10.1145/2370216.2370407 https://doi.org/10.1145/2370216.2370407

Sebbak F , Chibani A , Amirat Y , et al. , 2013 . An evidential fusion approach for activity recognition in ambient intelligence environments . Robot Auton Syst , 61 ( 11 ): 1235 - 1245 . https://doi.org/10.1016/j.robot.2013.05.010 https://doi.org/10.1016/j.robot.2013.05.010

Sehar U , Xiong J , Xia ZY , 2025 . Automatic tooth labeling after segmentation using prototype-based meta-learning . Mach Intell Res , 22 ( 3 ): 539 - 552 . https://doi.org/10.1007/s11633-024-1520-6 https://doi.org/10.1007/s11633-024-1520-6

Sensoy M , Kaplan L , Kandemir M , 2018 . Evidential deep learning to quantify classification uncertainty . Proc 32 nd Int Conf on Neural Information Processing Systems , p. 3183 - 3193 .

Shafer G , 1976 . A Mathematical Theory of Evidence . Princeton University Press , Princeton, USA .

Shafer G , 2016 . A mathematical theory of evidence turns 40 . Int J Approx Reason , 79 : 7 - 25 . https://doi.org/10.1016/j.ijar.2016.07.009 https://doi.org/10.1016/j.ijar.2016.07.009

Shi YS , Gong CY , Su ZG , 2025 . Time-series clustering with dynamic feature mining in the framework of belief function theory . IEEE Trans Syst Man Cybern Syst , 55 ( 9 ): 6136 - 6149 . https://doi.org/10.1109/tsmc.2025.3577606 https://doi.org/10.1109/tsmc.2025.3577606

Skau E , Armstrong C , Truong DP , et al. , 2023 . Open world Dempster-Shafer using complementary sets . Proc 13 th Int Symp on Imprecise Probability: Theories and Applications , p. 438 - 449 .

Smets P , 2005 . Decision making in the TBM: the necessity of the pignistic transformation . Int J Approx Reason , 38 ( 2 ): 133 - 147 . https://doi.org/10.1016/j.ijar.2004.05.003 https://doi.org/10.1016/j.ijar.2004.05.003

Song XF , Zhang Y , Gong DW , et al. , 2022 . A fast hybrid feature selection based on correlation-guided clustering and particle swarm optimization for high-dimensional data . IEEE Trans Cybern , 52 ( 9 ): 9573 - 9586 . https://doi.org/10.1109/TCYB.2021.3061152 https://doi.org/10.1109/TCYB.2021.3061152

Strat TM , 1990 . Decision analysis using belief functions . Int J Approx Reason , 4 ( 5-6 ): 391 - 417 . https://doi.org/10.1016/0888-613X(90)90014-S https://doi.org/10.1016/0888-613X(90)90014-S

Su ZG , Denœux T , 2019 . BPEC: belief-peaks evidential clustering . IEEE Trans Fuzzy Syst , 27 ( 1 ): 111 - 123 . https://doi.org/10.1109/TFUZZ.2018.2869125 https://doi.org/10.1109/TFUZZ.2018.2869125

Su ZG , Wang PH , 2014 . Regression analysis of belief functions on interval-valued variables: comparative studies . Soft Comput , 18 ( 1 ): 51 - 70 . https://doi.org/10.1007/s00500-013-1033-7 https://doi.org/10.1007/s00500-013-1033-7

Su ZG , Wang PH , Yu XJ , 2010 . Immune genetic algorithm-based adaptive evidential model for estimating unmeasured parameter: estimating levels of coal powder filling in ball mill . Expert Syst Appl , 37 ( 7 ): 5246 - 5258 . https://doi.org/10.1016/j.eswa.2009.12.077 https://doi.org/10.1016/j.eswa.2009.12.077

Su ZG , Wang YF , Wang PH , 2013 . Parametric regression analysis of imprecise and uncertain data in the fuzzy belief function framework . Int J Approx Reason , 54 ( 8 ): 1217 - 1242 . https://doi.org/10.1016/j.ijar.2013.02.008 https://doi.org/10.1016/j.ijar.2013.02.008

Su ZG , Denœux T , Hao YS , et al. , 2018 . Evidential K-NN classification with enhanced performance via optimizing a class of parametric conjunctive t-rules . Knowl-Based Syst , 142 : 7 - 16 . https://doi.org/10.1016/j.knosys.2017.11.020 https://doi.org/10.1016/j.knosys.2017.11.020

Su ZG , Hu QH , Denœux T , 2021 . A distributed rough evidential K-NN classifier: integrating feature reduction and classification . IEEE Trans Fuzzy Syst , 29 ( 8 ): 2322 - 2335 . https://doi.org/10.1109/TFUZZ.2020.2998502 https://doi.org/10.1109/TFUZZ.2020.2998502

Sun QR , Zhou YY , Gong PL , et al. , 2025 . Attention detection using EEG signals and machine learning: a review . Mach Intell Res , 22 ( 2 ): 219 - 238 . https://doi.org/10.1007/s11633-024-1492-6 https://doi.org/10.1007/s11633-024-1492-6

Sun RL , Deng Y , 2019 . A new method to identify incomplete frame of discernment in evidence theory . IEEE Access , 7 : 15547 - 15555 . https://doi.org/10.1109/ACCESS.2019.2893884 https://doi.org/10.1109/ACCESS.2019.2893884

Tang SW , Zhou ZJ , Hu CH , et al. , 2022 . A new evidential reasoning rule-based safety assessment method with sensor reliability for complex systems . IEEE Trans Cybern , 52 ( 5 ): 4027 - 4038 . https://doi.org/10.1109/TCYB.2020.3015664 https://doi.org/10.1109/TCYB.2020.3015664

Tang SW , Cao Y , Jiang J , et al. , 2023 . Modeling, optimization, and robustness analysis of evidential reasoning rule under multidiscernment framework . IEEE Trans Aerosp Electron Syst , 59 ( 6 ): 8981 - 8994 . https://doi.org/10.1109/TAES.2023.3312351 https://doi.org/10.1109/TAES.2023.3312351

Thabtah F , Hammoud S , Kamalov F , et al. , 2020 . Data imbalance in classification: experimental evaluation . Inform Sci , 513 : 429 - 441 . https://doi.org/10.1016/j.ins.2019.11.004 https://doi.org/10.1016/j.ins.2019.11.004

Tian HP , Zhang ZW , Martin A , et al. , 2022 . Reliability-based imbalanced data classification with Dempster-Shafer theory . Proc 7 th Int Conf on Belief Functions: Theory and Applications , p. 77 - 86 .

Tian SQ , Zhong YF , Ma AL , et al. , 2022 . Three-dimensional change detection in urban areas based on complementary evidence fusion . IEEE Trans Geosci Remote Sens , 60 : 5608913 . https://doi.org/10.1109/TGRS.2021.3101506 https://doi.org/10.1109/TGRS.2021.3101506

Tong Z , Xu P , Denœux T , 2019 . ConvNet and Dempster-Shafer theory for object recognition . Proc 13 th Int Conf on Scalable Uncertainty Management , p. 368 - 381 . https://doi.org/10.1007/978-3-030-35514-2_27 https://doi.org/10.1007/978-3-030-35514-2_27

Tong Z , Xu P , Denœux T , 2021a . An evidential classifier based on Dempster-Shafer theory and deep learning . Neurocomputing , 450 : 275 - 293 . https://doi.org/10.1016/j.neucom.2021.03.066 https://doi.org/10.1016/j.neucom.2021.03.066

Tong Z , Xu P , Denœux T , 2021b . Fusion of evidential CNN classifiers for image classification . Proc 6 th Int Conf on Belief Functions: Theory and Applications , p. 168 - 176 . https://doi.org/10.1007/978-3-030-88601-1_17 https://doi.org/10.1007/978-3-030-88601-1_17

Wang GX , Cheng G , Zhou PC , et al. , 2024 . Cross-level attentive feature aggregation for change detection . IEEE Trans Circ Syst Video Technol , 34 ( 7 ): 6051 - 6062 . https://doi.org/10.1109/TCSVT.2023.3344092 https://doi.org/10.1109/TCSVT.2023.3344092

Wang J , Zhou ZJ , Hu CH , et al. , 2022 . A new evidential reasoning rule with continuous probability distribution of reliability . IEEE Trans Cybern , 52 ( 8 ): 8088 - 8100 . https://doi.org/10.1109/TCYB.2021.3051676 https://doi.org/10.1109/TCYB.2021.3051676

Wang JY , Yu QZ , 2020 . A dynamic multi-sensor data fusion approach based on evidence theory and WOWA operator . Appl Intell , 50 ( 11 ): 3837 - 3851 . https://doi.org/10.1007/s10489-020-01739-8 https://doi.org/10.1007/s10489-020-01739-8

Wang LC , Wang ZD , Han QL , et al. , 2018 . Event-based variance-constrained H ∞ filtering for stochastic parameter systems over sensor networks with successive missing measurements . IEEE Trans Cybern , 48 ( 3 ): 1007 - 1017 . https://doi.org/10.1109/TCYB.2017.2671032 https://doi.org/10.1109/TCYB.2017.2671032

Wang M , Zhang XY , 2010 . Change detection using high spatial resolution remotely sensed imagery by combining evidence theory and structural similarity . J Remote Sens , 14 ( 3 ): 558 - 570 (in Chinese) . https://doi.org/10.11834/jrs.20100313 https://doi.org/10.11834/jrs.20100313

Wang X , Rong Y , Wang SA , et al. , 2025 . Unleashing the power of CNN and Transformer for balanced RGB-event video recognition . Mach Intell Res , 22 ( 6 ): 1031 - 1047 . https://doi.org/10.1007/s11633-025-1555-3 https://doi.org/10.1007/s11633-025-1555-3

Williams D , Liao XJ , Xue Y , et al. , 2007 . On classification with incomplete data . IEEE Trans Patt Anal Mach Intell , 29 ( 3 ): 427 - 436 . https://doi.org/10.1109/TPAMI.2007.52 https://doi.org/10.1109/TPAMI.2007.52

Wu DQ , Li HH , Hou YX , et al. , 2024 . Spatial-channel attention Transformer with pseudo regions for remote sensing image-text retrieval . IEEE Trans Geosci Remote Sens , 62 : 4704115 . https://doi.org/10.1109/TGRS.2024.3395313 https://doi.org/10.1109/TGRS.2024.3395313

Xiao FY , 2019 . Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy . Inform Fus , 46 : 23 - 32 . https://doi.org/10.1016/j.inffus.2018.04.003 https://doi.org/10.1016/j.inffus.2018.04.003

Xiao FY , 2020 . A new divergence measure for belief functions in D-S evidence theory for multisensor data fusion . Inform Sci , 514 : 462 - 483 . https://doi.org/10.1016/j.ins.2019.11.022 https://doi.org/10.1016/j.ins.2019.11.022

Xie MY , Han ZB , Zhang CQ , et al. , 2023 . Exploring and exploiting uncertainty for incomplete multi-view classification . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 19873 - 19882 . https://doi.org/10.1109/CVPR52729.2023.01903 https://doi.org/10.1109/CVPR52729.2023.01903

Xie YX , Qiu M , Zhang HB , et al. , 2022 . Gaussian distribution based oversampling for imbalanced data classification . IEEE Trans Knowl Data Eng , 34 ( 2 ): 667 - 679 . https://doi.org/10.1109/TKDE.2020.2985965 https://doi.org/10.1109/TKDE.2020.2985965

Xu NJ , Xue DZ , Qian SS , et al. , 2025 . Prompting large language models for automatic question tagging . Mach Intell Res , 22 ( 5 ): 917 - 928 . https://doi.org/10.1007/s11633-024-1509-1 https://doi.org/10.1007/s11633-024-1509-1

Xu PL , Cui YQ , Xiong W , 2025 . MH-T2TA: a multiple-hypothesis algorithm for multi-sensor track-to-track association with an intelligent track score . Front Inform Technol Electron Eng , 26 ( 11 ): 2231 - 2253 . https://doi.org/10.1631/FITEE.2400340 https://doi.org/10.1631/FITEE.2400340

Xu SX , Chen YF , Ma C , et al. , 2022 . Deep evidential fusion network for medical image classification . Int J Approx Reason , 150 : 188 - 198 . https://doi.org/10.1016/j.ijar.2022.08.013 https://doi.org/10.1016/j.ijar.2022.08.013

Xue GD , Chang Q , Wang J , et al. , 2023 . An adaptive neuro-fuzzy system with integrated feature selection and rule extraction for high-dimensional classification problems . IEEE Trans Fuzzy Syst , 31 ( 7 ): 2167 - 2181 . https://doi.org/10.1109/TFUZZ.2022.3220950 https://doi.org/10.1109/TFUZZ.2022.3220950

Yager RR , 1987 . On the Dempster-Shafer framework and new combination rules . Inform Sci , 41 ( 2 ): 93 - 137 . https://doi.org/10.1016/0020-0255(87)90007-7 https://doi.org/10.1016/0020-0255(87)90007-7

Yan BW , Lang CB , Cheng G , et al. , 2024 . Understanding negative proposals in generic few-shot object detection . IEEE Trans Circ Syst Video Technol , 34 ( 7 ): 5818 - 5829 . https://doi.org/10.1109/TCSVT.2024.3367666 https://doi.org/10.1109/TCSVT.2024.3367666

Yang DP , Guo YF , Xue YB , et al. , 2025 . Distributed kernel mean embedding Gaussian belief propagation for underwater multi-sensor multi-target passive tracking . Front Inform Technol Electron Eng , 26 ( 10 ): 2016 - 2029 . https://doi.org/10.1631/FITEE.2500204 https://doi.org/10.1631/FITEE.2500204

Yang LH , Ren TY , Ye FF , et al. , 2022 . An ensemble extended belief rule base decision model for imbalanced classification problems . Knowl-Based Syst , 242 : 108410 . https://doi.org/10.1016/j.knosys.2022.108410 https://doi.org/10.1016/j.knosys.2022.108410

Yu H , Chen LY , Yao JT , 2021 . A three-way density peak clustering method based on evidence theory . Knowl-Based Syst , 211 : 106532 . https://doi.org/10.1016/j.knosys.2020.106532 https://doi.org/10.1016/j.knosys.2020.106532

Yuan B , Yue XD , Lv Y , et al. , 2020 . Evidential deep neural networks for uncertain data classification . Proc 13 th Int Conf on Knowledge Science, Engineering and Management , p. 427 - 437 . https://doi.org/10.1007/978-3-030-55393-7_38 https://doi.org/10.1007/978-3-030-55393-7_38

Zhang CL , Zhou ZJ , Luo JY , et al. , 2023 . Statistical feature-based evidential reasoning for equipment health state assessment . IEEE Trans Aerosp Electron Syst , 59 ( 6 ): 8219 - 8234 . https://doi.org/10.1109/TAES.2023.3300297 https://doi.org/10.1109/TAES.2023.3300297

Zhang JF , Zhang QH , He X , et al. , 2021 . Compound-fault diagnosis of rotating machinery: a fused imbalance learning method . IEEE Trans Contr Syst Technol , 29 ( 4 ): 1462 - 1474 . https://doi.org/10.1109/TCST.2020.3015514 https://doi.org/10.1109/TCST.2020.3015514

Zhang JW , Wang ZC , Li SF , et al. , 2025 . Convolutional neural network aided movable antenna array design for channel estimation . Intell Robot , 5 ( 4 ): 844 - 58 . https://doi.org/10.20517/ir.2025.43 https://doi.org/10.20517/ir.2025.43

Zhang JX , Cui Q , Ma XJ , 2024 . Deep evidential remote sensing landslide image classification with a new divergence, multiscale saliency and an improved three-branched fusion . IEEE J Sel Top Appl Earth Obs Remote Sens , 17 : 3799 - 3820 . https://doi.org/10.1109/JSTARS.2024.3354455 https://doi.org/10.1109/JSTARS.2024.3354455

Zhang WA , Chen MZQ , Liu AD , et al. , 2017 . Aperiodic optimal linear estimation for networked systems with communication uncertainties . IEEE Trans Cybern , 47 ( 8 ): 2256 - 2265 . https://doi.org/10.1109/TCYB.2017.2684136 https://doi.org/10.1109/TCYB.2017.2684136

Zhang YJ , Peng DF , Huang X , 2018 . Object-based change detection for VHR images based on multiscale uncertainty analysis . IEEE Geosci Remote Sens Lett , 15 ( 1 ): 13 - 17 . https://doi.org/10.1109/lgrs.2017.2763182 https://doi.org/10.1109/lgrs.2017.2763182

Zhang Z , Zhou Y , Huang LQ , et al. , 2025 . Integrating local and global features via CNN and Mamba for semi-supervised change detection . IEEE Trans Instrum Meas , 74 : 5034715 . https://doi.org/10.1109/TIM.2025.3573775 https://doi.org/10.1109/TIM.2025.3573775

Zhang Z , Zhou Y , Huang LQ , et al. , 2026 . StyleDE-CD: dual-level style disentanglement for semi-supervised change detection . IEEE J Sel Top Appl Earth Obs Remote Sens , 19 : 14203 - 14218 .

Zhang ZW , Liu Z , Ma ZF , et al. , 2021 . Evidence integration credal classification algorithm versus missing data distributions . Inform Sci , 569 : 39 - 54 . https://doi.org/10.1016/j.ins.2021.04.008 https://doi.org/10.1016/j.ins.2021.04.008

Zhang ZW , Ye ST , Zhang YR , et al. , 2022a . Belief combination of classifiers for incomplete data . IEEE/CAA J Autom Sin , 9 ( 4 ): 652 - 667 . https://doi.org/10.1109/JAS.2022.105458 https://doi.org/10.1109/JAS.2022.105458

Zhang ZW , Tian HP , Yan LZ , et al. , 2022b . Learning a credal classifier with optimized and adaptive multiestimation for missing data imputation . IEEE Trans Syst Man Cybern Syst , 52 ( 7 ): 4092 - 4104 . https://doi.org/10.1109/tsmc.2021.3090210 https://doi.org/10.1109/tsmc.2021.3090210

Zhao C , Du DW , Hoogs A , et al. , 2023 . Open set action recognition via multi-label evidential learning . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 22982 - 22991 . https://doi.org/10.1109/CVPR52729.2023.02201 https://doi.org/10.1109/CVPR52729.2023.02201

Zhao JX , Liu SW , Wan JH , et al. , 2020 . Change detection method of high resolution remote sensing image based on D-S evidence theory feature fusion . IEEE Access , 9 : 4673 - 4687 . https://doi.org/10.1109/ACCESS.2020.3047915 https://doi.org/10.1109/ACCESS.2020.3047915

Zhou K , Martin A , Pan Q , et al. , 2015 . Median evidential c -means algorithm and its application to community detection . Knowl-Based Syst , 74 : 69 - 88 . https://doi.org/10.1016/j.knosys.2014.11.010 https://doi.org/10.1016/j.knosys.2014.11.010

Zhou K , Martin A , Pan Q , et al. , 2016 . ECMdd: evidential c -medoids clustering with mult iple prototypes . Patt Recogn , 60 : 239 - 257 . https://doi.org/10.1016/j.patcog.2016.05.005 https://doi.org/10.1016/j.patcog.2016.05.005

Zhou K , Martin A , Pan Q , et al. , 2018 . SELP: semi-supervised evidential label propagation algorithm for graph data clustering . Int J Approx Reason , 92 : 139 - 154 . https://doi.org/10.1016/j.ijar.2017.09.008 https://doi.org/10.1016/j.ijar.2017.09.008

Zhou K , Guo M , Martin A , 2022 . Evidential prototype-based clustering based on transfer learning . Int J Approx Reason , 151 : 322 - 343 . https://doi.org/10.1016/j.ijar.2022.10.001 https://doi.org/10.1016/j.ijar.2022.10.001

Zhou K , Zhang ZX , Xu HM , et al. , 2026 . Reliability analysis based on evidential likelihood for uncertain mixed Weibull distribution . IEEE Trans Reliab , 75 : 1020 - 1034 . https://doi.org/10.1109/TR.2026.3660916 https://doi.org/10.1109/TR.2026.3660916

Zhou N , Yao NM , Li QB , 2025 . Neural network based on inter-layer perturbation strategy for text classification . Mach Intell Res , 22 ( 1 ): 176 - 188 . https://doi.org/10.1007/s11633-024-1490-8 https://doi.org/10.1007/s11633-024-1490-8

Zhou QL , Pedrycz W , Liang Y , et al. , 2023 . Information granule based uncertainty measure of fuzzy evidential distribution . IEEE Trans Fuzzy Syst , 31 ( 12 ): 4385 - 4396 . https://doi.org/10.1109/TFUZZ.2023.3284713 https://doi.org/10.1109/TFUZZ.2023.3284713

Zhou QL , Deng Y , Yager RR , 2024 . CD-BFT: canonical decomposition-based belief functions transformation in possibility theory . IEEE Trans Cybern , 54 ( 1 ): 611 - 623 . https://doi.org/10.1109/TCYB.2023.3295179 https://doi.org/10.1109/TCYB.2023.3295179

Zhou QL , Pedrycz W , Deng Y , 2025a . Order-2 probabilistic information fusion on random permutation set . IEEE Trans Knowl Data Eng , 37 ( 2 ): 837 - 850 . https://doi.org/10.1109/TKDE.2024.3484009 https://doi.org/10.1109/TKDE.2024.3484009

Zhou QL , Zhu L , Deng Y , 2025b . Measure-based uncertainty with Dempster-Shafer structure . Sci China Inform Sci , 68 ( 10 ): 202204 . https://doi.org/10.1007/s11432-024-4563-x https://doi.org/10.1007/s11432-024-4563-x

Zhou XQ , Yue XD , Xu ZK , et al. , 2022 . PENet: prior evidence deep neural network for bladder cancer staging . Methods , 207 : 20 - 28 . https://doi.org/10.1016/j.ymeth.2022.08.010 https://doi.org/10.1016/j.ymeth.2022.08.010

Zhou ZJ , Hu CH , Hu GY , et al. , 2015 . Hidden behavior prediction of complex systems under testing influence based on semiquantitative information and belief rule base . IEEE Trans Fuzzy Syst , 23 ( 6 ): 2371 - 2386 . https://doi.org/10.1109/TFUZZ.2015.2426207 https://doi.org/10.1109/TFUZZ.2015.2426207

Zhou ZJ , Hu GY , Zhang BC , et al. , 2018 . A model for hidden behavior prediction of complex systems based on belief rule base and power set . IEEE Trans Syst Man Cybern Syst , 48 ( 9 ): 1649 - 1655 . https://doi.org/10.1109/TSMC.2017.2665880 https://doi.org/10.1109/TSMC.2017.2665880

Zhu HW , Basir O , 2005 . An adaptive fuzzy evidential nearest neighbor formulation for classifying remote sensing images . IEEE Trans Geosci Remote Sens , 43 ( 8 ): 1874 - 1889 . https://doi.org/10.1109/TGRS.2005.848706 https://doi.org/10.1109/TGRS.2005.848706

Zhu TY , Braytee A , Thiyagarajan K , et al. , 2026 . Autonomous detection of concrete cracks using self-supervised DinoV2 . Mach Intell Res , 23 ( 1 ): 168 - 184 . https://doi.org/10.1007/s11633-025-1553-5 https://doi.org/10.1007/s11633-025-1553-5

Zhu YC , Zhou K , Cuzzolin F , 2026 . TDCC: a trustworthy deep credal clustering method for uncertain data . IEEE Trans Cybern , 56 ( 5 ): 2876 - 2887 . https://doi.org/10.1109/TCYB.2026.3668393 https://doi.org/10.1109/TCYB.2026.3668393

Zhuang FZ , Qi ZY , Duan KY , et al. , 2021 . A comprehensive survey on transfer learning . Proc IEEE , 109 ( 1 ): 43 - 76 . https://doi.org/10.1109/JPROC.2020.3004555 https://doi.org/10.1109/JPROC.2020.3004555

Zouhal LM , Denœux T , 1998 . An evidence-theoretic k-NN rule with parameter optimization . IEEE Trans Syst Man Cybern Part C (App Rev) , 28 ( 2 ): 263 - 271 . https://doi.org/10.1109/5326.669565 https://doi.org/10.1109/5326.669565

Zuo KZ , Li XD , Dong YL , et al. , 2023 . Combination of different-granularity beliefs for sensor-based human activity recognition . IEEE Sens J , 23 ( 11 ): 12309 - 12319 . https://doi.org/10.1109/JSEN.2023.3266609 https://doi.org/10.1109/JSEN.2023.3266609

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