特征选择方法研究综述

施启军; 潘峰; 龙福海; 李娜娜; 苟辉朋; 苏浩辀; 谢雨寒

doi:10.19304/J.ISSN1000-7180.2021.1033

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特征选择方法研究综述

专题与综述 | 更新时间：2026-09-04

- 特征选择方法研究综述
- A review of feature selection methods
- 微电子学与计算机 2022年39卷第3期页码：1-8
- 作者机构：
  
  贵州民族大学机构数据科学与信息工程学院,贵州,贵阳,550025
- 作者简介：
- 基金信息：
- DOI：10.19304/J.ISSN1000-7180.2021.1033
  中图分类号：
- 纸质出版：2022
- 稿件说明：
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施启军, 潘峰, 龙福海, 李娜娜, 苟辉朋, 苏浩辀, 谢雨寒. 特征选择方法研究综述[J]. 微电子学与计算机, 2022, 39(3): 1-8.

SHI Qijun, PAN Feng, LONG Fuhai, et al. A review of feature selection methods[J]. 2022, 39(3): 1-8.
施启军, 潘峰, 龙福海, 李娜娜, 苟辉朋, 苏浩辀, 谢雨寒. 特征选择方法研究综述[J]. 微电子学与计算机, 2022, 39(3): 1-8. DOI： 10.19304/J.ISSN1000-7180.2021.1033.

SHI Qijun, PAN Feng, LONG Fuhai, et al. A review of feature selection methods[J]. 2022, 39(3): 1-8. DOI： 10.19304/J.ISSN1000-7180.2021.1033.

摘要

在大数据时代，特征选择是对数据进行预处理的必要环节.特征选择作为一种数据降维技术，其主要目的是从原始数据中选择出对算法最有益的相关特征，降低数据的维度和学习任务的难度，提升模型的效率.现阶段，有关特征选择算法方面的研究已取得阶段性成效，但也面临着重大挑战，其中维度灾难就是特征选择与分类问题所面临的重大挑战.首先，介绍了特征选择算法的基本架构

依次描述了子集的生成、子集的评估、终止条件、结果验证四个过程；其次，综述了特征选择领域的研究方法及研究成果，对特征选择方法分别依据评价策略、搜索策略、监督信息进行分类阐述，并对这些传统方法进行比较，指出它们的优势和不足；最后对特征选择进行了总结，并对其未来的研究方向进行了展望.

Abstract

In the era of big data

feature selection is a necessary part of data preprocessing. Feature selection is a data dimensionality reduction technology. Its main purpose is to select the most beneficial relevant features for the algorithm from the original data

reduce the dimensionality of the data and the difficulty of learning tasks

and improve the efficiency of the model. At this stage

the research on feature selection algorithms has achieved initial results

but it is also facing major challenges. Among them

the disaster of dimensionality is the major challenge faced by the feature selection and classification problem. First

the basic architecture of the feature selection algorithm is introduced

and the four processes of the generation of the subset

the evaluation of the subset

the termination condition

and the result verification are described in sequence Second

the methods are classified according to evaluation strategy

search strategy

and supervision information respectively

and compare these traditional methods

and point out their advantages and disadvantages. Finally

the feature selection is summarized and its future research direction is prospected.

关键词

Keywords

references

周红标, 乔俊飞. 基于高维k-近邻互信息的特征选择方法[J]. 智能系统学报, 2017, 12(5): 595-600. DOI: 10.11992/tis.201609020. ZHOU H B, QIAO J F. Feature selection method based on high dimensional k-nearest neighbors mutual information[J]. CAAI Transactions on Intelligent Systems, 2017, 12(5): 595-600. DOI: 10.11992/tis.201609020.

KOHAVI R, JOHN G H. Wrappers for feature subset selection[J]. Artificial Intelligence, 1997, 97(1-2): 273-324. DOI: 10.1016/S0004-3702(97)00043-X.

ESTEVEZ P A, TESMER M, PEREZ C A, et al. Normalized mutual information feature selection[J]. IEEE Transactions on Neural Networks, 2009, 20(2): 189-201. DOI: 10.1109/TNN.2008.2005601.

KIRA K, RENDELL L A. The feature selection problem: traditional methods and a new algorithm[C]//Proceedings of the Tenth National Conference on Artificial Intelligence. San Jose, California: AAAI Press, 1992.

WU X P, CHEN H M, LI T R, et al. Semi-supervised feature selection with minimal redundancy based on local adaptive[J]. Applied Intelligence, 2021, 51(11): 8542-8563. DOI: 10.1007/s10489-021-02288-4.

WUTZL B, LEIBNITZ K, RATTAY F, et al. Genetic algorithms for feature selection when classifying severe chronic disorders of consciousness[J]. PLoS One, 2019, 14(7): e0219683. DOI: 10.1371/journal.pone.0219683.

ALMUALLIM H, DIETTERICH T G. Learning Boolean concepts in the presence of many irrelevant features[J]. Artificial Intelligence, 1994, 69(1-2): 279-305. DOI: 10.1016/0004-3702(94)90084-1.

DASH M, LIU H. Consistency-based search in feature selection[J]. Artificial Intelligence, 2003, 151(1-2): 155-176. DOI: 10.1016/S0004-3702(03)00079-1.

董红斌, 滕旭阳, 杨雪. 一种基于关联信息熵度量的特征选择方法[J]. 计算机研究与发展, 2016, 53(8): 1684-1695. DOI: 10.7544/issn1000-1239.2016.20160172. DONG H B, TENG X Y, YANG X. Feature selection based on the measurement of correlation information entropy[J]. Journal of Computer Research and Development, 2016, 53(8): 1684-1695. DOI: 10.7544/issn1000-1239.2016.20160172.

BRUNATO M, BATTITI R. X-MIFS: exact mutual information for feature selection[C]//Proceedings of 2016 International Joint Conference on Neural Networks (IJCNN). Vancouver: IEEE, 2016. DOI: 10.1109/IJCNN.2016.7727644.

QUINLAN J R. Introduction of decision trees[J]. Machine Learning, 1986, 1(1): 81-106. DOI: 81

KWAK N, CHOI C H. Input feature selection by mutual information based on Parzen window[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(12): 1667-1671. DOI: 10.1109/TPAMI.2002.1114861.

SHEN K Q, ONG C J, LI X P, et al. Feature selection via sensitivity analysis of SVM probabilistic outputs[J]. Machine Learning, 2008, 70( 1): 1-20. DOI: 10. 1007/s 10994-007-5025-7.

LIU H, YU L. Toward integrating feature selection algorithms for classification and clustering[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(4): 491-502. DOI: 10.1109/TKDE.2005.66.

张翠军, 陈贝贝, 周冲, 等. 基于多目标骨架粒子群优化的特征选择算法[J]. 计算机应用, 2018, 38(11): 3156-3160. DOI: 10.11772/j.issn.1001-9081.2018041358. ZHANG C J, CHEN B B, ZHOU C, et al. Feature selection algorithm based on multi-objective bare-bones particle swarm optimization[J]. Journal of Computer Applications, 2018, 38(11): 3156-3160. DOI: 10.11772/j.issn.1001-9081.2018041358.

WU X X, CHENG Q. Top-k regularization for supervised feature selection[J]. arXiv: 2106.02197, 2021.

SETIONO R, LIU H. Neural-network feature selector[J]. IEEE Transactions on Neural Networks, 1997, 8(3): 654-662. DOI: 10.1109/72.572104.

WANG C, CHEN X J, YUAN G W, et al. Semisupervised feature selection with sparse discriminative least squares regression[J]. IEEE Transactions on Cybernetics, 202 1: 1- 12. DOI: 10. 1 109/TCYB.202 1.3060804.

PENG H C, LONG F H, DING C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(8): 1226-1238. DOI: 10.1109/TPAMI.2005.159.

张永波, 游录金, 陈杰新. 基于模拟退火的多标记数据特征选择[J]. 计算机工程与设计, 2011, 32(7): 2494-2496. DOI: 10.16208/j.issn1000-7024.2011.07.084. ZHANG Y B, YOU L J, CHEN J X. Feature selection for multi-label data by using simulated annealing[J]. Computer Engineering and Design, 2011, 32(7): 2494-2496. DOI: 10.16208/j.issn1000-7024.2011.07.084.

KOLLER D, SAHAMI M. Toward optimal feature selection[C]//Proceedings of the Thirteenth International Conference on International Conference on Machine Learning. Bari Italy, 1996.

KUMAGAI A, IWATA T, FUJIWARA Y. Few-shot learning for unsupervised feature selection[J]. arXiv: 2107.00816, 2021.

YANG J B, SHEN K Q, ONG C J, et al. Feature selection for MLP neural network: the use of random permutation of probabilistic outputs[J]. IEEE Transactions on Neural Networks, 2009, 20(12): 1911-1922. DOI: 10.1109/TNN.2009.2032543.

SOMOL P, PUDIL P, KITTLER J. Fast branch % bound algorithms for optimal feature selection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(7): 900-912. DOI: 10.1109/TPAMI.2004.28.

CAO T T, ZHANG M J, ANDREAE P, et al. A wrapper feature selection approach to classification with missing data[C]//Proceedings of the 19th European Conference on the Applications of Evolutionary Computation. Springer International Publishing. Porto, Portugal: Springer, 2016. DOI: 10.1007/978-3-319-31204-0_44.

BREIMAN L, FRIEDMAN J H, OLSHEN R A, et al. Classification and regression trees[J]. Biometrics, 1984, 40(3): 874. DOI: 10.2307/2530946.

叶婷婷, 刘明霞, 张道强. 基于有效距离的多模态特征选择[J]. 模式识别与人工智能, 2016, 29(7): 658-664. DOI: 10.16451/j.cnki.issn1003-6059.201607010. YE T T, LIU M X, ZHANG D Q. Effective distance based multi-modality feature selection[J]. Pattern Recognition and Artificial Intelligence, 2016, 29(7): 658-664. DOI: 10.16451/j.cnki.issn1003-6059.201607010.

叶志伟, 郑肇葆, 万幼川, 等. 基于蚁群优化的特征选择新方法[J]. 武汉大学学报·信息科学版, 2007, 32(12): 1127-1130. DOI: 10.3969/j.issn.1671-8860.2007.12.010. YE Z W, ZHENG Z B, WAN Y C, et al. A novel approach for feature selection based on ant colony optimization algorithm[J]. Geomatics and Information Science of Wunan University, 2007, 32(12): 1127-1130. DOI: 10.3969/j.issn.1671-8860.2007.12.010.

SINDHWANI V, RAKSHIT S, DEODHARE D, et al. Feature selection in MLPs and SVMs based on maximum output information[J]. IEEE Transactions on Neural Networks, 2004, 15(4): 937-948. DOI: 10.1109/TNN.2004.828772.

崔文岩, 孟相如, 李纪真, 等. 基于粗糙集粒子群支持向量机的特征选择方法[J]. 微电子学与计算机, 2015, 32(1): 120-123. DOI: 120 CUI W Y, MENG X R, LI J Z, et al. Feature selection based on RS-PSO-SVM[J]. Microelectronics % Computer, 2015, 32(1): 120-123. DOI: 120

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