Hybridizing Rough Sets and SVM for Credit Risk Evaluation

Different from the previous studies, a novel hybrid intelligent mining system is designed by hybridizing rough sets and support vector machines from a new perspective. In the proposed system, original information table is firstly reduced by rough sets from two-dimensional (attribute dimension and object dimension) reduction (2D-Reduction) view, and then support vector machines are used to extract typical features and to filter its noise and thus reduce the information table further. The goal of first step (i.e., 2D-Reduction) is to reduce the training burden and accelerate the learning process for support vector machines. Finally, the mined knowledge or classification rule sets are generated from the reduced information table by rough sets, rather than from the trained support vector machines. Therefore, the advantage of our proposed hybrid intelligent system is that it can overcome difficulty of extracting rules from a training support vector machine and possess the robustness which is lacking for rough set based approaches. To illustrate the effectiveness of the proposed system, two publicly credit datasets including both consumer and corporation credits are used.

The rest of the chapter is organized as follows. Section 4.2 describes some preliminaries about rough sets and support vector machine. In Section 4.3, the proposed hybrid intelligent mining system incorporating SVM into rough set is described and the algorithms to generate classification rules from information table are proposed. In Section 4.4, we compare and analyze some empirical results about two real-world credit datasets. Finally, some conclusions are drawn in Section 4.5.