Recent Study on the Application of Hybrid Rough Set and Soft Set Theories in Decision Analysis Process
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
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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. Keywords: Hybrid Rough set Soft set Parameter reduction Medical and big data
1 Introduction Decision analysis always deals with complex data that have different characteristics and structures. The most problematic data is categorized as uncertain data whereas the criteria value is practically difficult to be determined. This problem will remain unsolved if the decision-making process is involved with a complex data or more specifically, if it deals with a big data set [31]. A very powerful method needs to be selected in order to avoid the problem of ineffectiveness in the computational work and also to produce the best computational results. Based on the literature, one of the most popular approaches that has always been investigated is parameterization method. © Springer International Publishing Switzerland 2016 H. Fujita et al. (Eds.): IEA/AIE 2016, LNAI 9799, pp. 713–724, 2016. DOI: 10.1007/978-3-319-42007-3_61
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M. Mohamad and A. Selamat
This method helps decision makers to simplify a complex data set. Even though the best method has been selected to handle this kind of problem, it still has some disadvantages. For instance, the classical rough set theory which is a well-known method that is capable of handling complex problems [17] still needs assistance from other methods to deal with parameterization problem. Thus, various theories and concepts such as granular computing, deep learning, mathematical theories, artificial intelligent (AI), and hybrid approaches have been proposed in order to overcome these problems. The purposes of this study are: (i) to highlight the existing work
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