An Unbalanced Dataset Classification Approach Based on v-Support Vector Machine
Yinggang Zhao, Qinming He · 2006
Support Vector Machine (SVM) has been extensively studied and has shown remarkable success in many applications. However, when faced with unbalanced datasets, the SVM can not get ideal classification result and even in some cases the classification ability was very bad and unaccepted. The V-Support Vector Machine (V-SVM) is a new formulation of the regular SVM, and its parameter V has intuitive meanings compared with C (the penalty constant in SVM). By investigating the relation between SVM and V-SVM, we gave an equation between V and C, meanwhile we analyzed the factor behind the classification failure of SVM on unbalanced dataset. Then a classification algorithm based on V -SVM was addressed to overcome this inconvenience. Experimental results show the effectiveness of the proposed algorithm.