Mathematics and Statistics Vol. 10(4), pp. 759 - 772
DOI: 10.13189/ms.2022.100407
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Uncertainty Optimization-Based Rough Set for Incomplete Information Systems

Arvind Kumar Sinha , Pradeep Shende *
Department of Mathematics, National Institute of Technology Raipur, Chhattisgarh, India


Often the information in the surrounding world is incomplete, and such incomplete information gives rise to uncertainties. Pawlak's rough set model is an approach to approximation under uncertainty. It uses a tolerance relation to obtain single granulation of the incomplete information system for approximation. In this work, we extend the single granulation rough set for the incomplete information system to an uncertainty optimization-based rough set (UOBRS). The proposed approach is used to minimize the uncertainty using multiple tolerance relations. We list properties of the UOBRS for incomplete information systems. We compare UOBRS with the classical single granulation rough set (SGRS) and multi-granular rough set (MGRS). We list the basic properties of the UOBMGRS. We introduce the application of the UOBRS for attribute subset selection in case of incomplete information system. We use the measure of approximation quality to assess the uncertainties of the attributes. We compare the approximation quality of the attributes using UOBRS with the approximation quality using SGRS and MGRS. We get higher approximation quality with the less number of attributes using UOBRS as compared to SGRS and MGRS. The proposed method is a novel approach to dealing with incomplete information systems for more effective dataset analysis.

Incomplete Information System, Rough Set, Uncertainty Optimization, Approximation Quality, Feature Subset Selection

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Arvind Kumar Sinha , Pradeep Shende , "Uncertainty Optimization-Based Rough Set for Incomplete Information Systems," Mathematics and Statistics, Vol. 10, No. 4, pp. 759 - 772, 2022. DOI: 10.13189/ms.2022.100407.

(b). APA Format:
Arvind Kumar Sinha , Pradeep Shende (2022). Uncertainty Optimization-Based Rough Set for Incomplete Information Systems. Mathematics and Statistics, 10(4), 759 - 772. DOI: 10.13189/ms.2022.100407.