Ensemble of classification methods based on SVM and its application in diagnosis
Haitao Wang · Journal of Propulsion Technology · 2007
In order to solve the shortage problem of ensemble of classification using neural networks and decision trees as weak learner and improve the effect of ensemble of classification,a novel approach of classification ensemble named AdaBoost-SVM is presented,which uses SVM as weak learner for AdaBoost.To obtain a set of effective SVM weak learner,this algorithm adaptively adjusts the kernel parameter in SVM instead of using a fixed one.The practical applications in UCI repository and aeorengine faulty samples show that the proposed method solves the problem of selection difficuty for weak learner parameter and learning cycles in the existing AdaBoost methods and it has better generalization performance and is more fitting to classify the faulty samples scattered greatly.