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特征选择问题与元启发式算法：关于其公式化、评估及应用的系统文献综述

Feature Selection Problem and Metaheuristics: A Systematic Literature Review about Its Formulation, Evaluation and Applications.

作者信息

Barrera-García José, Cisternas-Caneo Felipe, Crawford Broderick, Gómez Sánchez Mariam, Soto Ricardo

机构信息

Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile.

Departamento de Electrotecnia e Informática, Universidad Técnica Federico Santa María, Federico Santa María 6090, Viña del Mar 2520000, Chile.

出版信息

Biomimetics (Basel). 2023 Dec 25;9(1):9. doi: 10.3390/biomimetics9010009.

DOI:10.3390/biomimetics9010009
PMID:38248583
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10813816/
Abstract

Feature selection is becoming a relevant problem within the field of machine learning. The feature selection problem focuses on the selection of the small, necessary, and sufficient subset of features that represent the general set of features, eliminating redundant and irrelevant information. Given the importance of the topic, in recent years there has been a boom in the study of the problem, generating a large number of related investigations. Given this, this work analyzes 161 articles published between 2019 and 2023 (20 April 2023), emphasizing the formulation of the problem and performance measures, and proposing classifications for the objective functions and evaluation metrics. Furthermore, an in-depth description and analysis of metaheuristics, benchmark datasets, and practical real-world applications are presented. Finally, in light of recent advances, this review paper provides future research opportunities.

摘要

特征选择正成为机器学习领域一个重要的问题。特征选择问题聚焦于选择能够代表特征全集的少量、必要且充分的特征子集，去除冗余和无关信息。鉴于该主题的重要性，近年来对该问题的研究蓬勃发展，产生了大量相关研究。基于此，本研究分析了2019年至2023年（2023年4月20日）发表的161篇文章，重点关注问题的表述和性能度量，并对目标函数和评估指标提出分类。此外，还对元启发式算法、基准数据集和实际应用进行了深入描述与分析。最后，鉴于近期的进展，本综述论文提供了未来的研究机会。

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