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Fast attribute reduction by neighbor inconsistent pair selection for dynamic decision tables

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Abstract

Attribute reduction is capable of reducing the dimensionality of data and improving the performance of data mining. As a reasonable representative of relationships between samples, neighbor inconsistent pair focuses on measuring uncertainty in information systems. Nevertheless, classical attribute reduction methods are static and unsuitable for data with variations. Additionally, it is inevitable for data to undergo changes in real-life scenarios, such as an increase in the number of samples. Therefore, it is essential to identify an efficient method for reducing the dimensionality of the dataset while preserving the classification accuracy. Inspired by these deficiencies, our focus lies on developing effective and efficient incremental methods that employ the neighbor inconsistent pair selection strategy for decision tables involving object variations. At first, some concepts related to rough sets, simplified decision tables and neighbor inconsistent pairs are introduced. Then, the heuristic attribute reduction algorithms for dynamic decision tables with the variation of object sets are designed by neighbor inconsistent pairs. Next, a novel feature selection procedure, which we refer to as incremental neighbor inconsistent pair selection, is proposed to update reducts for dynamic decision tables with the variation of object sets. Finally, two incremental attribute reduction algorithms based on neighbor inconsistent pair selection are designed. Furthermore, experiments are conducted on real datasets to validate the effectiveness and benefits of the proposed incremental algorithms. The results indicate that our algorithms exhibit minimal computing time requirements while achieving the highest classification accuracy among at least ten out of thirteen datasets when compared to the comparative algorithms.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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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 (2018RS3065, 2018TP1018).

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Authors and Affiliations

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

    Chucai Zhang, Hong Liu, Zhengxiang Lu & Jianhua Dai

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

    Chucai Zhang & Jianhua Dai

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

    Jianhua Dai

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

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Zhang, C., Liu, H., Lu, Z. et al. Fast attribute reduction by neighbor inconsistent pair selection for dynamic decision tables. Int. J. Mach. Learn. & Cyber. 15, 739–756 (2024). https://doi.org/10.1007/s13042-023-01931-5

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