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

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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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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Nos. 61976089, 61473259, 61070074, 60703038), the Hunan Provincial Science & Technology Project Foundation (2018TP1018, 2018RS3065) and the Construct Program of the Key Discipline in Hunan Province.

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Authors and Affiliations

  1. Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education), School of Mathematics and Statistics, Hunan Normal University, Changsha, 410081, Hunan, China

    Xiaofeng Liu & Jianhua Dai

  2. Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, 410081, Hunan, China

    Xiaofeng Liu, Jianhua Dai, Jiaolong Chen & Chucai Zhang

  3. College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, Hunan, China

    Xiaofeng Liu, Jianhua Dai, Jiaolong Chen & Chucai Zhang

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  1. Xiaofeng Liu
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  2. Jianhua Dai
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  4. Chucai Zhang
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Correspondence to Jianhua Dai.

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Liu, X., Dai, J., Chen, J. et al. Unsupervised attribute reduction based on \(\alpha \)-approximate equal relation in interval-valued information systems. Int. J. Mach. Learn. & Cyber. 11, 2021–2038 (2020). https://doi.org/10.1007/s13042-020-01091-w

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