计算机工程与应用2011，Vol.47Issue(33)：129-133,5.DOI:10.3778/j.issn.1002-8331.2011.33.038
不平衡支持向量机的惩罚因子选择方法
Error-cost selection for biased support vector machines
摘要
Abstract
Standard SVM often performs poorly on unbalanced 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 unbalanced datasets,better than the other methods for comparison.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Key words
sequential minimal optimization/biased-Support Vector Machines (SVM)/average density/error cost/parameter selection分类
信息技术与安全科学引用本文复制引用
金鑫,李玉鑑..不平衡支持向量机的惩罚因子选择方法[J].计算机工程与应用,2011,47(33):129-133,5.基金项目
国家自然科学基金(the National Natural Science Foundation of China under Grant No.61175004,No.60775010) （the National Natural Science Foundation of China under Grant No.61175004,No.60775010）
北京市自然科学基金(No.4112009) （No.4112009）
北京市教委科技发展项目(No.KZ201210005007) （No.KZ201210005007）
北京工业大学高层次人才培养项目. （）
