关键词: 支持向量机, K近邻法, 不平衡数据集

Abstract: Improved KNN-SVM that combined Support Vector Machine（SVM） with K Nearest Neighbor（KNN） is presented to improve the accuracy of imbalanced classification nearby SVM hyperplane. In the class phase, the algorithm computes the distance from the tested sample to the optimal super-plane of SVM in the feature space. If the distance is greater than the given threshold, the tested sample will be classified on SVM；otherwise the SVs from different categories are used as the tested sample of nearest neighbors, the tested sample will be classified on KNN. A large amount of experiments by the UCI dataset show that the algorithm can significantly improve the identification rate of the minority samples and overall classification performance.

Key words: Support Vector Machine（SVM）, K Nearest Neighbor（KNN）, imbalanced datasets