Recent Study on the Application of Hybrid Rough Set and Soft Set Theories in Decision Analysis Process

Abstract

Many approaches and methods have been proposed and applied in decision analysis process. One of the most popular approaches that has always been investigated is parameterization method. This method helps decision makers to simplify a complex data set. The purpose of this study was to highlight the roles and the implementations of hybrid rough set and soft set theories in decision-making especially in parameter reduction process. Rough set and soft set theories are the two powerful mathematical tools that have been successfully proven by many research works as a good parameterization method. Both of the theories have the capability of handling data uncertainties and data complexity problems. Recent studies have also shown that both rough set and soft set theories can be integrated together in solving different problems by producing a variety of algorithms and formulations. However, most of the existing works only did the performance validity test with a small volume of data set. In order to prove the hypothesis, which is the hybridization of rough set and soft set theories could help to produce a good result in the classification process, a new alternative to hybrid parameterization method is proposed as the outcome of this study. The results showed that the proposed method managed to achieve significant performance in solving the classification problem compared to other existing hybrid parameter reduction methods.