Credit scoring with F-score based on support vector machine

Abstract

Credit risk management is one of the most important issues in financial research. Reliable credit scoring models are crucial for financial agencies to evaluate credit applications. In this article, a novel feature-weighted support vector machine credit scoring models are presented for credit risk assessment, in which F-score and improved F-score is adopted for feature importance calculating. These featureweighted versions of Support Vector Machine are tested against the traditional feature selection Support Vector Machine on two real-world datasets and the research results reveal the validity of the proposed method. The feature-weighted methods have optimized performance, which improved the accuracy and reduced the modeling time consumption.