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International Journal of Machine Learning and Cybernetics

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04-05-2022 | Original Article

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

Authors: Jiaojiao Niu, Degang Chen

Published in: International Journal of Machine Learning and Cybernetics | Issue 9/2022

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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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Title: Incremental calculation approaches for granular reduct in formal context with attribute updating
Authors: Jiaojiao Niu
Degang Chen
Publication date: 04-05-2022
Publisher: Springer Berlin Heidelberg
Published in: International Journal of Machine Learning and Cybernetics / Issue 9/2022
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI: https://doi.org/10.1007/s13042-022-01561-3

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