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Incremental calculation approaches for granular reduct in formal context with attribute updating

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Abstract

Attribute reduction in formal concept analysis is a highly concerned dimensionality reduction method, which purifies formal context by removing unimportant attributes. Current trends of dealing with attribute reduction problem for large-scale datasets is mainly based on object updating, thus, ignore the fact that attributes may also be modified with evolving time. With that in mind, this study considers the attribute reduction of the data with attribute dynamic environments. Specifically, we first analyze the incremental mechanism of granular reduct in a formal context, as well as develop the corresponding incremental algorithms. Then, in a consistent formal decision context, we address the consistency-based incremental attribute reduction problem on the premise that the decision attribute set remains unchanged. In addition, to obtain a smaller reduction, attribute significance is defined to measure the identification ability of attributes to inconsistent objects. Different from the existing methods, the algorithms proposed in this paper can realize dynamic calculation of granular reduct and the numerical experiments conducted show that the algorithm proposed in this paper is more efficient than other algorithms in the face of large-scale datasets. In the meantime, the generated granular reduct can improve the accuracy of classifiers in the classification task.

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Acknowledgements

This work was supported by the National Key R&D Program of China (2020YFB1707802) and the National Natural Science Foundation of China (No. 12071131).

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  1. School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, China

    Jiaojiao Niu

  2. School of Mathematics and Physics, North China Electric Power University, Beijing, 102206, China

    Degang Chen

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Niu, J., Chen, D. Incremental calculation approaches for granular reduct in formal context with attribute updating. Int. J. Mach. Learn. & Cyber. 13, 2763–2784 (2022). https://doi.org/10.1007/s13042-022-01561-3

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