Multi-class SVMs Based on Probability Voting Strategy and Its Application
Xiaohong Wang · Jisuanji gongcheng · 2009
Traditional Support Vector Machine(SVM)is originally designed for binary classification.How to effectively extend it to multi-class classification is still an on-going research issue.After analysis and comparison of the problems and defections of the existing One-Versus-One(OVO)methods of multi-class SVMs,the novel multi-class classification method based on probability voting strategy is put forward.In the new strategy,the differences and different weights among these two-class SVM classifiers are considered and combined with the value of voting.The presented multi-class SVMs method can achieve the better classification ability and resolve the unclassifiable region problems in the conventional Max-Wins-Voting(MWV)strategy.Moreover,the multi-class SVMs method based on probability voting is used as the key technology of fault diagnosis for gearbox.The practical results show that compared with traditional MWV strategy,the presented one is effective.