Initialization of Feature Selection Search for Classification

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
María Luque-Rodriguez, José Molina-Baena, Alfonso Jiménez-Vílchez, A. Arauzo-Azofra
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引用次数: 0

Abstract

Selecting the best features in a dataset improves accuracy and efficiency of classifiers  in a learning process. Datasets generally have more features than necessary, some of  them being irrelevant or redundant to others. For this reason, numerous feature selection  methods have been developed, in which different evaluation functions and measures are  applied. This paper proposes the systematic application of individual feature evaluation  methods to initialize search-based feature subset selection methods. An exhaustive review  of the starting methods used by genetic algorithms from 2014 to 2020 has been carried out.  Subsequently, an in-depth empirical study has been carried out evaluating the proposal for  different search-based feature selection methods (Sequential forward and backward selection,  Las Vegas filter and wrapper, Simulated Annealing and Genetic Algorithms). Since  the computation time is reduced and the classification accuracy with the selected features  is improved, the initialization of feature selection proposed in this work is proved to be  worth considering while designing any feature selection algorithms. 
初始化特征选择搜索分类
在数据集中选择最佳特征可以提高分类器在学习过程中的准确性和效率。数据集通常具有比必要的更多的特征,其中一些特征与其他特征无关或冗余。因此,已经开发了许多特征选择方法,其中应用了不同的评估函数和度量。本文提出系统地应用个体特征评价方法初始化基于搜索的特征子集选择方法。对2014年至2020年遗传算法的启动方法进行了详尽的回顾。随后,对不同的基于搜索的特征选择方法(顺序正向和向后选择、拉斯维加斯滤波和包装、模拟退火和遗传算法)进行了深入的实证研究。由于减少了计算时间,并提高了所选特征的分类精度,因此在设计任何特征选择算法时,本文提出的特征选择初始化是值得考虑的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research 工程技术-计算机:人工智能
CiteScore
9.60
自引率
4.00%
发文量
98
审稿时长
4 months
期刊介绍: JAIR(ISSN 1076 - 9757) covers all areas of artificial intelligence (AI), publishing refereed research articles, survey articles, and technical notes. Established in 1993 as one of the first electronic scientific journals, JAIR is indexed by INSPEC, Science Citation Index, and MathSciNet. JAIR reviews papers within approximately three months of submission and publishes accepted articles on the internet immediately upon receiving the final versions. JAIR articles are published for free distribution on the internet by the AI Access Foundation, and for purchase in bound volumes by AAAI Press.
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