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25-02-2020 | Original Article | Issue 9/2020

International Journal of Machine Learning and Cybernetics 9/2020

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

Journal:
International Journal of Machine Learning and Cybernetics > Issue 9/2020
Authors:
Xiaofeng Liu, Jianhua Dai, Jiaolong Chen, Chucai Zhang
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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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