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A novel approach to attribute reduction and rule acquisition of formal decision context

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Abstract

Rule acquisition and attribute reduction are important research topics in formal concept analysis. Many existing rule-based attribute reduction algorithms are designed to computing all reductions by using discernibility functions and therefore these algorithms are NP-hard. To improve the applicability of rule-based attribute reduction algorithms, firstly, we propose a method to simplify the discernibility matrix such that fewer concepts need to be distinguished. Then a heuristics approach is presented to compute one reduction by using the ordered attributes method. In addition, a novel rule acquisition algorithm for OW-decision rules is presented. Some comparative analyses of the rule acquisition algorithm with the existing algorithms are examined which shows that the algorithms presented in this study behave well. And finally, we select some datasets from UCI datasets for taking experiments and illustrate the effectiveness and efficiency of our proposed reduction algorithms.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 61976130).

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Authors and Affiliations

  1. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China

    Qian Hu

  2. School of Mathematics, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China

    Keyun Qin & Han Yang

  3. Westone Information Industry INC., Chengdu, Sichuan, 610031, China

    Binbin Xue

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  1. Qian Hu
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  2. Keyun Qin
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  4. Binbin Xue
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Correspondence to Qian Hu.

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This work is supported by the Natural Science Foundation (Grants No.: 61976130)

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Hu, Q., Qin, K., Yang, H. et al. A novel approach to attribute reduction and rule acquisition of formal decision context. Appl Intell 53, 13834–13851 (2023). https://doi.org/10.1007/s10489-022-04139-2

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