Fuzzy Rough Set and Information Entropy 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 to credit scoring feature selection based on fuzzy-rough model and information entropy is proposed. Three UCI credit datasets are selected to demonstrate the competitive performance of the presented method comparing with some other methods. Experiments show the proposed method get a better performance comparing with the classical rough set approaches.