Some Information Measures for Fuzzy Rough Sets

Omdutt Sharma, Pratiksha Tiwari and Priti Gupta
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Omdutt Sharma: PDM University, India
Pratiksha Tiwari: Delhi Institute of Advanced Studies, India
Priti Gupta: Maharshi Dayanand University, India

International Journal of Fuzzy System Applications (IJFSA), 2021, vol. 10, issue 2, 82-102

Abstract: Information theory is a tool to measure uncertainty; these days, it is used to solve various challenging problems that involve hybridization of information theory with the fuzzy set, rough sets, vague sets, etc. In order to solve challenging problems in scientific data analysis and visualization recently, various authors are working on hybrid measures of information theory. In this paper, using the relation between information measures, some measures are proposed for the fuzzy rough set. Firstly, an entropy measure is derived using the fuzzy rough similarity measure, and then corresponding to this entropy measure, some other measures like mutual information measure, joint entropy measure, and conditional entropy measure are also proposed. Some properties of these measures are also studied. Later, the proposed measure is compared with some existing measures to prove its efficiency. Further, the proposed measures are applied to pattern recognition, medical diagnoses, and a real-life decision-making problem for incorporating software in the curriculum at the Department of Statistics.

Date: 2021
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