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Knowledge granularity reduction for decision tables

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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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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant no. 61972052) and the Discipline Team support Program of Beijing Language and Culture University (Grant no. GF201905).

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  1. School of Information Science, Beijing Language and Culture University, Beijing, China

    Guilong Liu & Yanbin Feng

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  1. Guilong Liu
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  2. Yanbin Feng
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Correspondence to Guilong Liu.

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Liu, G., Feng, Y. Knowledge granularity reduction for decision tables. Int. J. Mach. Learn. & Cyber. 13, 569–577 (2022). https://doi.org/10.1007/s13042-020-01254-9

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  • DOI: https://doi.org/10.1007/s13042-020-01254-9

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