Exploiting Hybrid Kernel-Based Fuzzy Complementary Mutual Information for Selecting Features

被引：0
|
作者：
Yuan Z. [1 ]
Chen H. [1 ]
Wang Z. [1 ]
Li T. [1 ]
机构：
[1] School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756）（National Engineering Laboratory of Integrated Transportation Big Data Application Technology (Southwest Jiaotong University), Chengdu 611756
来源：
Jisuanji Yanjiu yu Fazhan/Computer Research and Development | 2023年 / 60卷 / 05期
基金：
中国国家自然科学基金;
关键词：
complementary entropy; feature selection; fuzzy rough set theory; hybrid kernel; uncertainty measure;
D O I：
10.7544/issn1000-1239.202111272
中图分类号：
学科分类号：
摘要：
Fuzzy rough set theory is currently receiving a lot of attention in the fields of data mining and machine learning. The theory provides an effective tool to overcome the discretization problem and can be applied directly to numerical or mixed attribute data. In the fuzzy rough set model, fuzzy relations are defined to measure the similarity between objects and numerical attribute values no longer need to be discretized. The theory has been successfully applied to many fields such as attribute reduction, rule extraction, cluster analysis and outlier detection. Information entropy has been introduced into fuzzy rough set theory for the representation of fuzzy and uncertainty information, resulting in different forms of fuzzy uncertainty measures such as fuzzy information entropy, fuzzy complementary entropy, and fuzzy mutual information. However, most of the proposed fuzzy mutual information on decisions is non-monotonic, which may lead to a non-convergent learning algorithm. To this end, the fuzzy complementary mutual information on decisions is defined based on the hybrid kernel fuzzy complementary entropy, which is shown to vary monotonically with features. Then, the feature selection method is explored by using the hybrid kernel-based fuzzy complementary mutual information and a corresponding algorithm is designed. Experimental results show that the proposed algorithm can select fewer features and maintain or improve the classification accuracy in most cases. © 2023 Science Press. All rights reserved.
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页码：1111 / 1120
页数：9
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