An improved P-SVM method usedto deal with imbalanced data sets
Potential Support Vector Machine (P-SVM) is a novel Support Vector Machine (SVM) method. It defines a new optimization model which is different from standard SVM. However, P-SVM method has restrictions in dealing with unbalanced data sets. To solve this problem, an improved P-SVM method used to deal with imbalanced data sets is proposed in this paper. By using different penalty parameters to different slack variables in P-SVM, the new algorithm adjusts penalty parameters more flexible, and effectively improves the low classification accuracy caused by imbalanced samples.From theoretical analyses and experimental results, they have shown that this new method can obtain better classification accuracy than standard SVM and P-SVM in dealing with imbalanced data sets.
P-SVM penalty parameter slack variable optimization model imbalanced data sets
CHEN Li CHEN Jing GAO Xin-tao
Department of Basic Subjects College of Information & Business,Zhongyuan University of Technology,Zh College of Sciences China Agricultural University Beijing,China Culture Staff Room Henan Industrial Technician College Zhengzhou,Henan,China
国际会议
上海
英文
118-122
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）