关键词: 不平衡数据集, 权重分配模型, 支持向量机（SVM）

Abstract: Based on analyzing the shortages of SVM（Support Vector Machine） algorithm in solving classification problems on imbalanced dataset, a novel SVM approach based on cluster-weight technology and based-grading SVM classifier（short as WSVM） is presented in this paper that considers the uneven distribution of training sample between classes and within classes. The specific steps are as follows：when preprocessing, it uses K-means algorithm based on weight assignment model to obtain the weights of the majority samples. Classification is consisted of three phases. It selects the located in each cluster boundary majority samples, which is equal with the minority samples in quantity, then classifies the minority samples and selects samples, and adjusts the initial classifier through the unselected majority samples. When it comes to satisfy the explicit stopping criteria, the final classifier is got. A large amount of experiments by the UCI dataset show that WSVM can significantly improve the identification rate of the minority samples and overall classification performance.

Key words: imbalanced dataset, weight assignment model, Support Vector Machine（SVM）