DOI: 10.3724/SP.J.1087.2009.00833

Journal of Computer Applications (计算机应用) 2009/29:3 PP.833-835

Multi-feature fusion method based on support vector machine and k-nearest neighbor classifier

The traditional classification methods only use one single classifier, which may lead to one-sidedness, low accuracy, and that the samples nearby the Support Vector Machine (SVM) hyperplanes are more easily misclassified. To solve these problems, the multi-feature fusion method based on SVM and K-Nearest Neighbor (KNN) classifiers was presented in this paper. Firstly, the features were divided into L groups and the SVM hyperplanes were constructed for each feature of training set. Secondly, the testing set was tested by SVM-KNN method, and the decision profile matrixes were obtained. Finally, these decision profile matrixes were implemented by multi-feature fusion method. The experimental results on Iris data show that the forecast accuracy of the multi-feature fusion method based on SVM-KNN classifiers increases by 28.7% and 1.9% than those of SVM and SVM-KNN methods respectively.

Key words:Support Vector Machine (SVM),K-Nearest Neighbor (KNN) algorithm,multi-feature fusion,inverse probability

ReleaseDate:2014-07-21 14:31:31

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