Abstract
An approach of binary tree fuzzy multi-class support vector machines algorithm was proposed due to the multi-classification problem and sensitivity to the noisy data of the Support Vector Machine algorithm(SVM).The algorithm introduced a K-Nearest Neighbor(KNN) fuzzy membership function according to Binary Tree Support Vector Machine algorithm(BTSVM).Depending on the different influences of respective data set on the classification results and measuring method of the KNN fuzzy membership function,it can calculate the corresponding value and additionally obtain the different penalty value and ignore the unimportant data for the classification results,while constructing the classification hyper-planes.Experimental results indicate that the algorithm has a better ability of anti-interference and better classification effects.
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