Fuzzy support vector machine for imbalanced data with borderline noise

This work is an extension of the Fuzzy Support Vector Machines for Class Imbalance Learning (FSVM-CIL) method proposed by Rukshan Batuwita and Vasile Palade. For FSVMs, a very important part is the fuzzy function transforming different distance measures to membership values between 0 and 1. The larger the membership value, the more important the corresponding training data point. Although various variants have been proposed recently, few have discussed proper fuzzy functions. This work first shows the limitations of fuzzy functions in original FSVM-CIL for imbalanced data with noise around the between-class borderline (noted as borderline noise in this paper), and then, a new fuzzy function, named the Gaussian fuzzy function, is proposed and explained in detail. Modifications are also made to the current distance measures. Experiments on several public imbalanced datasets show the effectiveness of the proposed methods through the comparison with FSVM-CIL and several other popular approaches for imbalanced data.