关键词: 支持向量机, 不平衡数据, 过采样, 灰狼优化算法

Abstract: In order to improve the classification effect of traditional Support Vector Machine (SVM) for minority classes in unbalanced datasets, an over sampling method based on Improved Gray Wolf algorithm (IGWO),  called SMOTE,  is proposed. Firstly, the generation form of the initial gray wolf population is improved, and the individual gray wolf is composed of the penalty factor, kernel parameter, eigenvector and sampling rate of minority classes of SVM. Then, the optimal correlation parameters and the optimal sampling rate combination are obtained by intelligent search of gray wolf optimization process, and resampling is performed for the prediction of the classifier learning machine. Through the empirical test of six UCI datasets, it is concluded that IGWOSMOTE+SVM can improve the classification accuracy of minority classes by 6.3% and the overall classification accuracy by 2.1% compared with the traditional SMOTE+SVM model. IGWOSMOTE can be used as a new over sampling classification method.

Key words: support vector machine；unbalanced data；over sampling；gray wolf optimization algorithm ,