面向特征选择的智能优化算法综述

doi:10.3969/j.issn.1006-2475.2025.02.05

摘要/Abstract

摘要： 特征选择作为数据预处理的主要技术之一，可有效识别关键特征，从而降维以有效应对“维度诅咒”问题。特征选择是典型的NP-hard问题，智能优化算法因其卓效的全局搜索能力被广泛应用于特征选择。首先，本文整理了特征重要度评估方法和参数更新方法，前者用于判断特征的相关性与冗余性，后者用于算法的参数更新,二者均可用于面向特征选择的智能优化算法的各个核心步骤。然后，介绍了算法初始化、种群搜索、目标函数设计3个核心步骤的策略设计。从决策空间初始化和种群初始化2个方面归纳了初始化策略，并分析不同策略优势与局限；从种群数量出发，对单种群和多种群的搜索策略进行细致划分；根据目标函数应用的指标不同，分类总结目标函数设计。最后，讨论了面向特征选择的智能优化算法未来研究方向。

关键词: 特征选择, 智能优化算法, 初始化策略, 搜索策略, 目标函数

Abstract: Feature selection， as one of the main techniques in data preprocessing， can effectively identify key features， thereby reducing dimensionality and effectively addressing the issue of “curse of dimensionality”. Feature selection is a typical NP-hard problem， and intelligent optimization algorithm have been widely employed in feature selection due to their remarkable global search ability. Firstly， this paper summarizes methods for evaluating feature importance and parameters updating. The former is used for evaluating the relevance and redundancy of features， while the latter is used for updating algorithm parameters. These two methodologies are both applicable to various crucial steps of intelligent optimization algorithm for feature selection. Then， the strategic design of three core steps in the process， namely algorithm initialization， population search， and objective function design， is introduced. The initialization strategy is summarized from the perspectives of decision space initialization and population initialization， with an analysis of the advantages and limitations of different strategies. Based on the population quantity， a detailed classification of search strategies for single population and multiple population is provided. According to the different metrics applied in the objective function， a categorization of objective function design can be summarized. Finally， it discusses future work for intelligent optimization algorithm to feature selection.

Key words: feature selection, intelligent optimization algorithm, initialization strategy, search strategy, objective function

中图分类号:

TP301.6

齐浩淳. 面向特征选择的智能优化算法综述[J]. 计算机与现代化, 2025, 0(02): 33-43.

QI Haochun. Survey on Intelligent Optimization Algorithm for Feature Selection[J]. Computer and Modernization, 2025, 0(02): 33-43.

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