关键词: 信息熵, 分布特性, 支持向量数据描述, 分类

Abstract: Most of Support Vector Data Description (SVDD) methods have blindness and bias issues when working on two-class problems. The authors proposed a new SVDD method based on information entropy. In this algorithm, firstly, the entropy values were resolved respectively of the two classes of samples. Secondly, according to the size of the value, one class was placed inside the ball. Finally, the penalty was given based on the information provided by the sizes of the two sample data and their entropy values. The efficiency of this algorithm was verified by using artificial data and UCI datasets for the data imbalanced classification problem. The experimental results on artificial data sets and UCI data sets show the feasibility and effectiveness of the proposed method.

Key words: information entropy, distribution character, Support Vector Data Description (SVDD), classification