Evaluation of Feature Selection Methods for Predictive Modeling Using Neural Networks in Credits Scoring

A credit-risk evaluation decision involves processing huge volumes of raw data, and hence requires powerful data mining tools. Several techniques that were developed in machine learning have been used for financial credit-risk evaluation decisions. Data mining is the process of finding patterns and relations in large databases. Neural Networks are one of the popular tools for building predictive models in data mining. The major drawback of neural network is the curse of dimensionality which requires optimal feature subset. Feature selection is an important topic of research in data mining. Feature selection is the problem of choosing a small subset of features that optimally is necessary and sufficient to describe the target concept. In this research an attempt has been made to investigate the preprocessing framework for feature selection in credit scoring using neural network. Feature selection techniques like best first search, info gain etc. methods have been evaluated for the effectiveness of the classification of the risk groups on publicly available data sets. In particular, German, Australian, and Japanese credit rating data sets have been used for evaluation. The results have been conclusive about the effectiveness of feature selection for neural networks and validate the hypothesis of the research.