Rough set and Tabu search based feature selection for credit scoring

As the credit industry has been growing rapidly, huge number of consumers’ credit data are collected by the credit department of the bank and credit scoring has become a very important issue. Usually, a large amount of redundant information and features are involved in the credit dataset, which leads to lower accuracy and higher complexity of the credit scoring model. So, effective feature selection methods are necessary for credit dataset with huge number of features. In this paper, a novel approach, called FSRT, to feature selection based on rough set and tabu search is proposed. In FSRT, conditional entropy is regarded as the heuristic to search the optimal solutions. The proposed method is introduced to credit scoring and Japan credit dataset in UCI database is selected to demonstrate the competitive performance of the proposed method. Moreover, FSRT shows a superior performance in saving the computational costs and improving classification accuracy