A new fuzzy support vector machines for class imbalance learning

A bilateral-weighted fuzzy support vector machine (B-FSVM) proposed by Wang is to evaluate bank's credit risk. However, it also suffers from the problem of class imbalance datasets in most cases. In this paper, we present a method to impve B-FSVM for class imbalance learning (called NFSVM-CIL) to handle the class imbalance problem in the presence of outliroers and noise. We evaluate and compare its performance with support vector machine, fuzzy support vector machine and FSVM-CIL.