计算机工程与应用2015，Vol.51Issue(24)：126-131,6.DOI:10.3778/j.issn.1002-8331.1311-0448
基于KNN算法的改进的一对多SVM多分类器
Improved KNN-based 1-vs-all SVM multi-class classifier
摘要
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 corre-sponding 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 con-suming 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.关键词
支持向量机(SVM)/一对多/K近邻(KNN)/数据偏斜Key words
Support Vector Machine(SVM)/1-vs-all/K Nearest Neighbour(KNN)/data skew分类
信息技术与安全科学引用本文复制引用
刘雨康,张正阳,陈琳琳,陈静..基于KNN算法的改进的一对多SVM多分类器[J].计算机工程与应用,2015,51(24):126-131,6.基金项目
国家自然科学基金(No.11271367). （No.11271367）
