Abstract 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.

中文翻译：

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具有边界噪声的不平衡数据的模糊支持向量机

摘要 这项工作是对 Rukshan Batuwita 和 Vasile Palade 提出的类不平衡学习模糊支持向量机 (FSVM-CIL) 方法的扩展。对于FSVMs，一个非常重要的部分是模糊函数将不同的距离度量转换为0到1之间的隶属度值。隶属度值越大，对应的训练数据点就越重要。尽管最近提出了各种变体，但很少有人讨论适当的模糊函数。这项工作首先展示了原始 FSVM-CIL 中模糊函数对于类间边界线周围有噪声（本文中称为边界噪声）的不平衡数据的局限性，然后，一个新的模糊函数，称为高斯模糊函数，是提出并详细解释。还对当前的距离度量进行了修改。