人类多任务处理
计算机科学
任务(项目管理)
特征选择
群体行为
选择(遗传算法)
领域(数学)
特征(语言学)
简单(哲学)
算法
人工智能
遗传算法
机器学习
数学
工程类
认识论
哲学
语言学
认知心理学
系统工程
纯数学
心理学
作者
Lingjie Li,Manlin Xuan,Qiuzhen Lin,Min Jiang,Ming Zhong,Kay Chen Tan
出处
期刊:Cornell University - arXiv
日期:2022-12-17
标识
DOI:10.48550/arxiv.2212.08854
摘要
Recently, evolutionary multitasking (EMT) has been successfully used in the field of high-dimensional classification. However, the generation of multiple tasks in the existing EMT-based feature selection (FS) methods is relatively simple, using only the Relief-F method to collect related features with similar importance into one task, which cannot provide more diversified tasks for knowledge transfer. Thus, this paper devises a new EMT algorithm for FS in high-dimensional classification, which first adopts different filtering methods to produce multiple tasks and then modifies a competitive swarm optimizer to efficiently solve these related tasks via knowledge transfer. First, a diversified multiple task generation method is designed based on multiple filtering methods, which generates several relevant low-dimensional FS tasks by eliminating irrelevant features. In this way, useful knowledge for solving simple and relevant tasks can be transferred to simplify and speed up the solution of the original high-dimensional FS task. Then, a competitive swarm optimizer is modified to simultaneously solve these relevant FS tasks by transferring useful knowledge among them. Numerous empirical results demonstrate that the proposed EMT-based FS method can obtain a better feature subset than several state-of-the-art FS methods on eighteen high-dimensional datasets.
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