大连理工大学学报2016，Vol.56Issue(5)：525-531,7.DOI:10.7511/dllgxb201605013
用于不平衡数据分类的模糊支持向量机算法
A fuzzy support vector machine algorithm for imbalanced data classification
摘要
Abstract
As an effective machine learning technique,support vector machine (SVM)has been successfully applied to various fields.However,when it comes to imbalanced datasets,SVM produces suboptimal classification models.On the other hand,the SVM algorithm is very sensitive to noise and outliers present in the datasets.To overcome the disadvantages of imbalanced and noisy training datasets,a novel fuzzy SVM algorithm for imbalanced data classification is proposed.When designing the fuzzy membership function,the proposed algorithm takes into account not only the distance between the training sample and its class center,but also the tightness around the training sample. Experimental results show that the proposed fuzzy SVM algorithm can effectively handle the imbalanced and noisy problem.关键词
支持向量机/模糊支持向量机/模糊隶属度/不平衡数据/分类Key words
support vector machine (SVM)/fuzzy support vector machine/fuzzy membership/imbalanced data/classification分类
信息技术与安全科学引用本文复制引用
鞠哲,曹隽喆,顾宏..用于不平衡数据分类的模糊支持向量机算法[J].大连理工大学学报,2016,56(5):525-531,7.基金项目
国家自然科学基金资助项目(61502074,U1560102) （61502074,U1560102）
高等学校博士学科点专项科研基金资助项目(20120041110008) （20120041110008）
