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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 3/2022

14-01-2021 | Original Article

Knowledge granularity reduction for decision tables

Authors: Guilong Liu, Yanbin Feng

Published in: International Journal of Machine Learning and Cybernetics | Issue 3/2022

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Abstract

Attribute reduction is a difficult topic in rough set theory and knowledge granularity reduction is one of the important types of reduction. However, up to now, its reduction algorithm based on a discernibility matrix has not been given. In this paper, we show that knowledge granularity reduction is equivalent to both positive region reduction and X-absolute reduction, and derive its corresponding algorithm based on a discernibility matrix to fill the gap. Particularly, knowledge granularity reduction is the usual positive region reduction for consistent decision tables. Finally, we provide a simple knowledge granularity reduction algorithm for finding a reduct with the help of binary integer programming, and consider six UCI datasets to illustrate our algorithms.
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Metadata
Title
Knowledge granularity reduction for decision tables
Authors
Guilong Liu
Yanbin Feng
Publication date
14-01-2021
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 3/2022
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-020-01254-9

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