Credit Scoring Model based on Weighted Voting and Cluster based Feature Selection

Abstract

Credit scoring concerns with developing empirical model to support financial decision making process for financial institutions. It makes use of applicants' historical data and statistical or machine learning techniques to access the risk associated with an applicant. However, the data may have redundant and irrelevant information and features, which degrades the classification accuracy and increases the complexity. So, effective feature selection technique can resolve the problem of credit scoring dataset with huge number of features. In various studies, it is shown that ensemble classifier improves the classification performances as compared to its base classifiers. This study focuses to combine the benefits of feature selection and ensemble framework. For feature selection an approach based on feature clustering have been proposed in this study. Moreover, dataset with selected features is applied on five base classifiers and output obtained by base classifiers are aggregated by weighted voting approach for prediction of final output. For validating the proposed approach, three real world credit scoring datasets are utilized and results compared with some existing feature selection techniques in terms of classification accuracy and F1 -score.