Multi-Label Feature Selection Based on Improved Ant Colony Optimization Algorithm with Dynamic Redundancy and Label Dependence
作者机构：School of ComputerHubei University of TechnologyWuhan430068China Xining Big Data Service AdministrationXining810000China Wuhan Fiberhome Technical Services Co.Ltd.Wuhan430205China
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2024年第81卷第10期
页 面：1157-1175页
核心收录：
学科分类：08[工学] 0835[工学-软件工程] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by National Natural Science Foundation of China(Grant Nos.62376089,62302153,62302154,62202147) the key Research and Development Program of Hubei Province,China(Grant No.2023BEB024)
主 题：Multi-label feature selection ant colony optimization algorithm dynamic redundancy high-dimensional data label correlation
摘 要：The world produces vast quantities of high-dimensional multi-semantic ***,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and *** selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant *** ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement ***,existing methods do not consider crucial correlation information,such as dynamic redundancy and label *** tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label ***,the dynamic redundancy is assessed between the selected feature subset and potential ***,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label *** results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper.
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