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）

摘要： SVM在处理不平衡数据分类问题（class imbalance problem）时，其分类结果常倾向于多数类。为此，综合考虑类间不平衡和类内不平衡，提出一种基于聚类权重的分阶段支持向量机（WSVM）。预处理时，采用K均值算法得到多数类中各样本的权重。分类时，第一阶段根据权重选出多数类内各簇边界区域的与少数类数目相等的样本；第二阶段对选取的样本和少数类样本进行初始分类；第三阶段用多数类中未选取的样本对初始分类器进行优化调整，当满足停止条件时，得到最终分类器。通过对UCI数据集的大量实验表明，WSVM在少数类样本的识别率和分类器的整体性能上都优于传统分类算法。

关键词: 不平衡数据集, 权重分配模型, 支持向量机（SVM）