基于支持向量机与反K近邻的分类算法研究

针对支持向量机在对样本进行分类时，决策超平面附近的点较易错分的问题，首先将反K近邻法引入分类问题，提出了反K近邻分类算法；然后，将支持向量机（SVM）与反K近邻分类算法（RKNN）相结合，提出了基于支持向量机与反K近邻的分类算法（SVM-RKNN）；最后，为了避免单一分类器可能存在的片面性问题，提出了基于SVM-RKNN的多特征融合分类方法。实验结果表明：SVM-RKNN分类算法的分类准确率比SVM方法平均提高了2.13%，而基于SVM-RKNN的多特征融合分类算法的分类准确率分别比SVM和SVM-RKNN算法平均提高了2.54%和0.41%。

Classification algorithm research based on support vector machine and reverse K-nearest neighbor

When Support Vector Machine（SVM） is used to solve the classification problems,the samples nearby the SVM hyperplanes are more easily misclassified.To solve this problem,the Reverse K-Nearest Neighbor method is introduced into the classification problems,and the Reverse K-Nearest Neighbor classification method（RKNN） is presented.And then,a new classification algorithm based on Support Vector Machine and Reverse K-Nearest Neighbor classification method（SVM-RKNN） is presented.At last,in order to avoid the one-sidedness problems which may be produced by one single classifier,the multi-fusion method based on SVM-RKNN is presented.The experimental results show that the average forecast accuracy of the SVM-RKNN method increases 2.13% than the SVM method,and the average forecast accuracy of the multi-fusion method based on SVM-RKNN increases 2.54% and 0.41% than the SVM and SVM-RKNN method respectively.