微阵列数据集的特征选择技术：综合评述、分类和未来方向

Kulanthaivel BALAKRISHNAN; Ramasamy DHANALAKSHMI

doi:10.1631/FITEE.2100569

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微阵列数据集的特征选择技术：综合评述、分类和未来方向

常规文章 | Updated：2022-10-28

- 微阵列数据集的特征选择技术：综合评述、分类和未来方向
- Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions
- 信息与电子工程前沿(英文版) 2022年23卷第10期页码：1451-1478
- Affiliations：
  
  Department of Computer Science and Engineering, Indian Institute of Information Technology, Tiruchirappalli 620012, India
- Author bio：
  
  E-mail: bala.k.btech@gmail.com
  ‡Corresponding author
- Funds：
  
  the Department of Science and Technology under the Interdisciplinary Cyber-Physical Systems Scheme(T-54)
- DOI：10.1631/FITEE.2100569
  中图分类号： TP391
- 收稿：2021-12-10，
  
  修回：2022-07-04，
  
  纸质出版：2022-10-0
- Accepted：
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Kulanthaivel BALAKRISHNAN, Ramasamy DHANALAKSHMI. 微阵列数据集的特征选择技术：综合评述、分类和未来方向[J]. 信息与电子工程前沿(英文版), 2022,23(10):1451-1478.

Kulanthaivel BALAKRISHNAN, Ramasamy DHANALAKSHMI. Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(10): 1451-1478.
Kulanthaivel BALAKRISHNAN, Ramasamy DHANALAKSHMI. 微阵列数据集的特征选择技术：综合评述、分类和未来方向[J]. 信息与电子工程前沿(英文版), 2022,23(10):1451-1478. DOI： 10.1631/FITEE.2100569.

Kulanthaivel BALAKRISHNAN, Ramasamy DHANALAKSHMI. Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(10): 1451-1478. DOI： 10.1631/FITEE.2100569.

摘要

为获得最佳结果，从微阵列数据集中检索相关特征已成为特征选择（FS）技术的研究热点。本综述旨在全面阐述各种最新特征选择技术，同时介绍了基于微阵列数据集的处理多类分类问题的技术以及提高学习算法性能的不同方法。我们试图理解和解决数据集不平衡问题，以证实研究人员在微阵列数据集上的工作。对文献的分析为理解和强调在通过各种特征选择技术寻找最佳特征子集时存在的众多挑战和问题铺平了道路。同时提供了一个案例说明该方法的实施过程，该方法使用3个微阵列癌症数据集评估一些包装方法和混合方法的分类精度和收敛能力，以确认最优特征子集。

Abstract

For optimal results

retrieving a relevant feature from a microarray dataset has become a hot topic for researchers involved in the study of feature selection (FS) techniques. The aim of this review is to provide a thorough description of various

recent FS techniques. This review also focuses on the techniques proposed for microarray datasets to work on multiclass classification problems and on different ways to enhance the performance of learning algorithms. We attempt to understand and resolve the imbalance problem of datasets to substantiate the work of researchers working on microarray datasets. An analysis of the literature paves the way for comprehending and highlighting the multitude of challenges and issues in finding the optimal feature subset using various FS techniques. A case study is provided to demonstrate the process of implementation

in which three microarray cancer datasets are used to evaluate the classification accuracy and convergence ability of several wrappers and hybrid algorithms to identify the optimal feature subset.

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