摘要
征选择是数据挖掘和机器学习中的一项重要任务,能够降低数据的维度,提高学习算法的性能。进化计算算法通过模拟自然界生物进化机制完成搜索问题的最优解决方案,近年来在特征选择问题中得到了广泛应用,并取得了一定的成功。首先介绍了特征选择的基本框架;然后从进化计算特征选择方法的搜索机制、子集评价策略和目标数等方面进行了分析和总结;最后讨论了当前基于进化计算的特征选择方法面临的问题和挑战以及未来进一步的研究方向。
Feature selection was an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm.Evolutionary computing algorithms recently gained much attention and shown some success in feature selection problems in recent years by simulating the natural biological evolution mechanism to complete the optimal solution of the search problem.The basic framework of feature selection was introduced first.Then the search mechanism,subset evaluation strategy and objective number of feature selection methods based on evolutionary computation were analyzed and summarized.Finally,current issues and challenges were also discussed to identify promising areas for future research.
作者
王艳丽
梁静
薛冰
岳彩通
WANG Yanli;LIANG Jing;XUE Bing;Yue Caitong(School of Electrical Engineering,Zhengzhou University,Zhengzhou 450001,China;School of Engineering and Computer Science,Victoria University of Wellington,Wellington 6140,New Zealand)
出处
《郑州大学学报（工学版）》
CAS
北大核心
2020年第1期49-57,共9页
Journal of Zhengzhou University（Engineering Science）
基金
国家自然科学基金资助项目(61922072,61876169
61673404)
河南省高等学校重点科研项目(20B120002)河南省高等学校青年骨干教师培养计划项目。
关键词
分类
进化计算
特征选择
classification
evolutionary computation
feature selection
