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Semi-supervised attribute reduction via attribute indiscernibility

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Abstract

Attribute reduction based on rough sets plays an important role in data preprocessing. Discernibility pair, as an effective information measurement, has received extensive attention in attribute reduction. Unfortunately, the existing attribute importance measurement strategies based on discernibility pairs do not apply well to partially labeled data. Meanwhile, most of the existing attribute reduction algorithms focus on the relationships between objects and neglect the relationships between attributes, which may bring highly redundant attributes. Under the background of rough set theory, this paper studies the issue of semi-supervised attribute reduction, i.e. attribute reduction for partially labeled data. Firstly, we introduce the concept of discernibility pair based on object indiscernibility and propose a semi-supervised attribute reduction algorithm via the maximum discernibility pair by combining supervised and unsupervised discernibility pair strategies. Secondly, considering the relationships between attributes, we put forward new methods to define the similarity and distinction between attributes by discernibility pairs. Thirdly, we propose a semi-supervised attribute reduction algorithm by indiscernible attribute classes. Finally, comparative experiments indicate that the proposed algorithms are effective.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (61976089), the Major Program of the National Social Science Foundation of China (20&ZD047), the Natural Science Foundation of Hunan Province (2021JJ30451, 2022JJ30397), and the Hunan Provincial Science & Technology Project Foundation (2018RS 3065, 2018TP1018).

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Authors and Affiliations

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

    Jianhua Dai, Weisi Wang, Chucai Zhang & Shaojun Qu

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

    Jianhua Dai, Weisi Wang, Chucai Zhang & Shaojun Qu

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  1. Jianhua Dai
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  2. Weisi Wang
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  3. Chucai Zhang
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  4. Shaojun Qu
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Correspondence to Shaojun Qu.

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Dai, J., Wang, W., Zhang, C. et al. Semi-supervised attribute reduction via attribute indiscernibility. Int. J. Mach. Learn. & Cyber. 14, 1445–1464 (2023). https://doi.org/10.1007/s13042-022-01708-2

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