摘要
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 de-sign,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 de-tailed classification of search strategies for single population and multiple population is provided.According to the different met-rics applied in the objective function,a categorization of objective function design can be summarized.Finally,it discusses fu-ture work for intelligent optimization algorithm to feature selection.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Key words
feature selection/intelligent optimization algorithm/initialization strategy/search strategy/objective function分类
计算机与自动化
