Multi-Label Feature Selection Based on Improved Ant Colony Optimization Algorithm with Dynamic Redundancy and Label Dependence

作     者：Cai, Ting Ye, Chun Ye, Zhiwei Chen, Ziyuan Mei, Mengqing Zhang, Haichao Bai, Wanfang Zhang, Peng 

作者机构：School of Computer Hubei University of Technology Wuhan 430068 China Xining Big Data Service Administration Xining 810000 China Wuhan Fiberhome Technical Services Co. Ltd Wuhan 430205 China 

出 版 物：《Computers, Materials and Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第81卷第1期

页      面：1157-1175页

核心收录：

学科分类：1205[管理学-图书情报与档案管理] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Funding Statement: This research was supported by National Natural Science Foundation of China (Grant Nos. 62376089  62302153  62302154  62202147) and the key Research and Development Program of Hubei Province  China (Grant No. 2023BEB024) 

主　　题：Ant colony optimization 

摘      要：The world produces vast quantities of high-dimensional multi-semantic data. However, extracting valuable information fromsuch a largeamount of high-dimensional andmulti-label data is undoubtedly arduous and challenging. Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features. The ant colony optimization algorithmhas demonstrated encouraging outcomes in multi-label feature selection, because of its simplicity, efficiency, and similarity to reinforcement learning. Nevertheless, existing methods do not consider crucial correlation information, such as dynamic redundancy and label correlation. To 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 correlation. Initially, the dynamic redundancy is assessed between the selected feature subset and potential ***, the ant colony optimization algorithmextracts label correlation fromthe label set,which is then combined into the heuristic factor as label weights. Experimental results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony, outperforming the other algorithms involved in the paper. © 2024 The Authors. Published by Tech Science Press.