面向多类不平衡学习的一对多海林格距离决策树研究

董明刚; 刘明; 敬超

doi:10.1631/FITEE.2000417

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面向多类不平衡学习的一对多海林格距离决策树研究

常规文章 | Updated：2022-04-19

- 面向多类不平衡学习的一对多海林格距离决策树研究
  Enhanced Publication
- One-against-all-based Hellinger distance decision tree for multiclass imbalanced learning
- 信息与电子工程前沿（英文） 2022年23卷第2期页码：278-290
- Affiliations：
  
  1.School of Information Science and Engineering, Guilin University of Technology, Guilin541004, China
  2.Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin541004, China
  3.Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin541004, China
- Author bio：
  
  ‡Corresponding author
- Funds：
  
  National Natural Science Foundation of China(61802085;61563012);Guangxi Provincial Natural Science Foundation, China(2021GXNSFAA220074;2020GXNSFAA159038);Guangxi Key Laboratory of Embedded Technology and Intelligent System Foundation, China(2018A-04);Guangxi Key Laboratory of Trusted Software Foundation, China(kx202011)
- DOI：10.1631/FITEE.2000417
  中图分类号： TP301
- 收稿：2020-08-17，
  
  录用：2021-02-14，
  
  纸质出版：2022-02-0
- Accepted：
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董明刚, 刘明, 敬超. 面向多类不平衡学习的一对多海林格距离决策树研究[J]. 信息与电子工程前沿（英文）, 2022,23(2):278-290.

Minggang DONG, Ming LIU, Chao JING. One-against-all-based Hellinger distance decision tree for multiclass imbalanced learning[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(2): 278-290.
董明刚, 刘明, 敬超. 面向多类不平衡学习的一对多海林格距离决策树研究[J]. 信息与电子工程前沿（英文）, 2022,23(2):278-290. DOI： 10.1631/FITEE.2000417.

Minggang DONG, Ming LIU, Chao JING. One-against-all-based Hellinger distance decision tree for multiclass imbalanced learning[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(2): 278-290. DOI： 10.1631/FITEE.2000417.

摘要

由于传统机器学习方法对偏斜分布很敏感，且未考虑多类不平衡问题的特点，多类偏斜分布对机器学习算法来说是一个巨大挑战。为解决这一问题，提出一种新的基于一对多的海林格距离（OAHD）决策树分割准则。OAHD主要由两部分组成。首先，将一对多思想集成到OAHD的海林格距离计算过程中，从而对海林格距离决策树进行扩展，使其能解决多类不平衡问题。其次，针对多类不平衡问题，考虑了不同类的分布和数量，设计了改进的基尼系数。此外，对OAHD的性质进行理论证明，包括偏斜不敏感性和在决策树中寻找更纯节点的能力。最后，从基于进化学习的知识抽取（KEEL）和加州大学欧文分校（UCI）数据库中收集20个公开的真实不平衡数据集进行实验。实验结果表明，与其他5种常用决策树相比，OAHD在精度、F值，和多类别接收者操作特征曲线下面积（MAUC）上有显著优势。此外，使用了Friedman和Nemenyi检验，统计结果表明OAHD优于其他5种决策树。

Abstract

Since traditional machine learning methods are sensitive to skewed distribution and do not consider the characteristics in multiclass imbalance problems

the skewed distribution of multiclass data poses a major challenge to machine learning algorithms. To tackle such issues

we propose a new splitting criterion of the decision tree based on the one-against-all-based Hellinger distance (OAHD). Two crucial elements are included in OAHD. First

the one-against-all scheme is integrated into the process of computing the Hellinger distance in OAHD

thereby extending the Hellinger distance decision tree to cope with the multiclass imbalance problem. Second

for the multiclass imbalance problem

the distribution and the number of distinct classes are taken into account

and a modified Gini index is designed. Moreover

we give theoretical proofs for the properties of OAHD

including skew insensitivity and the ability to seek a purer node in the decision tree. Finally

we collect 20 public real-world imbalanced data sets from the Knowledge Extraction based on Evolutionary Learning (KEEL) repository and the University of California

Irvine (UCI) repository. Experimental and statistical results show that OAHD significantly improves the performance compared with the five other well-known decision trees in terms of Precision

F-measure

and multiclass area under the receiver operating characteristic curve (MAUC). Moreover

through statistical analysis

the Friedman and Nemenyi tests are used to prove the advantage of OAHD over the five other decision trees.

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