关键词: 序列最小最优化, 不平衡支持向量机, 平均密度, 惩罚因子, 参数选取

Abstract: Standard SVM often performs poorly on imbalanced datasets，whereas biased-SVM can deal with the problem using two different error costs.This paper explains why SVM fails，discusses how to solve a biased-SVM，and proposes a direct method to determine the error costs，i.e.，“average density”，in order to reduce the time needed for their selection via traditional cross validation.Experimental results show that the average density method can efficiently and effectively improve the performance of biased-SVM on imbalanced datasets，better than the other methods for comparison.

Key words: sequential minimal optimization, biased-Support Vector Machines（SVM）, average density, error cost, parameter selection