关键词: 支持向量机（SVM）, 一对多, K近邻（KNN）, 数据偏斜

Abstract: To solve the problems of heavy computation, time consuming, data skew and  the errors made in the category prediction of the points near the optimal hyperplane existing in one of the traditional Support Vector Machine（SVM） multi-class classification algorithms—1-vs-all, an improved method is proposed. For the given k-class-dataset, this paper takes all the data belonging to rare categories as the sparse point, wraps the dense points of each category with the corresponding super ball, enlarges the super ball on the principle of the balance between positive and negative samples in it and the radius minimum, and then trains the samples in each super ball for the corresponding SVM classifier. In the testing stage, it predicts the test points in the dense area under the traditional 1-vs-all category prediction criterion and uses the K Nearest Neighbour（KNN） algorithm for the category prediction of the test points in sparse area. In terms of the time consuming and classification precision in test result, the improved algorithm is better than the traditional one. It is proven that, to some degree, the improved algorithm can solve the above problems existing in traditional 1-vs-all algorithm.

Key words: Support Vector Machine（SVM）, 1-vs-all, K Nearest Neighbour（KNN）, data skew