Corporate and personal credit scoring via fuzzy non-kernel SVM with fuzzy within-class scatter

Abstract

Nowadays, the effective credit scoring becomes a very crucial factor for gaining competitive advantages in credit market for both customers and corporations. In this paper, we propose a credit scoring method which combines the non-kernel fuzzy 2-norm quadratic surface SVM model, T-test feature weighting strategy and fuzzy within-class scatter together. It is worth pointing out that this new method not only saves computational time by avoiding choosing a kernel and corresponding parameters in the classical SVM models, but also addresses the "curse of dimensionality" issue and improves the robustness. Besides, we develop an efficient way to calculate the fuzzy membership of each training point by solving a linear programming problem. Finally, we conduct several numerical tests on two benchmark data sets of personal credit and one real-world data set of corporation credit. The numerical results strongly demonstrate that the proposed method outperforms eight state-of-the-art and commonly-used credit scoring methods in terms of accuracy and robustness.

A kernel-free fuzzy support vector machine with Universum

“…Support vector machine (SVM) [7], which is proposed with good theoretical basis, is a well-known kernel-based technique for classification problems. It has been found out to be quite an effective tool in a variety of fields such as credit risk assessment [17,18], power forecasting [12] and hydrology [22]. Take credit risk assessment as an example, this problem can be reduced to a binary classification problem in essential.…”