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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 9/2020

25-02-2020 | Original Article

Unsupervised attribute reduction based on \(\alpha \)-approximate equal relation in interval-valued information systems

Authors: Xiaofeng Liu, Jianhua Dai, Jiaolong Chen, Chucai Zhang

Published in: International Journal of Machine Learning and Cybernetics | Issue 9/2020

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Abstract

As generalizations of single-valued information systems, interval-valued information systems (IVISs) can better express real data. At present, numerous unsupervised attribute reduction approaches for single-valued information systems have been considered, but there are few researches on unsupervised attribute reduction for IVISs. In this article, we investigate a new fuzzy relation by means of similarity between interval values, and propose the concept of \(\alpha \)-approximate equal relation in view of the fuzzy similarity class. Then the equivalence relation induced by \(\alpha \)-approximate equal relation is used to define the information entropy, which is used to construct the unsupervised attribute reduction method together with mutual information for IVISs. Finally, experiments demonstrate that the advanced unsupervised attribute reduction method is effective and feasible in IVISs.
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Metadata
Title
Unsupervised attribute reduction based on -approximate equal relation in interval-valued information systems
Authors
Xiaofeng Liu
Jianhua Dai
Jiaolong Chen
Chucai Zhang
Publication date
25-02-2020
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 9/2020
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-020-01091-w

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