An improved P-SVM method used to 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 unbalanced 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