An Ensemble Classifier Based on Structural Support Vector Machine for Imbalanced Data
YUAN Xing-Mei1,2,YANG Ming1,YANG Yang3
1.School of Computer Science and Technology,Nanjing Normal University,Nanjing 210023 2.Office of Information Construction and Management,Nanjing Institute of Technology,Nanjing 211167 3.Honor School,Nanjing Normal University,Nanjing 210023
Abstract To improve the performance of Support Vector Machine(SVM) classifier for imbalanced data,an ensemble classifier model based on structural SVM is introduced by incorporating cost-sensitive strategy. In the proposed classifier model,the training data is partitioned into several group by Ward hierarchical clustering algorithm,the structure information hidden in data is obtained,and the weight of every sample is initialized by using the prior knowledge hidden in clusters. Furthermore,employing AdaBoost strategy,the weight of each sample is dynamically adjusted effectively,and the weights of minority class samples are relatively increased. Hence,the cost of the misclassified positive samples is also increased for improving the classification accuracy of positive samples(minority class samples). The experimental results show that the proposed model effectively improves the classification performance of the imbalanced data.
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2013, Vol. 26 
