Revising method for separation hyperplane of imbalanced data in SVM;

Instance Importance Based SVM for Solving Imbalanced Data Classification

An adjustment method of the offset of separating hyperplane for binary-classification imbalanced data is proposed in order to balance and decrease the separating error ratio of the two classes of imbalanced data.

Secondly it presented the developed sampling methods regarding imbalanced data set and produced the classified methods.

A simple but systematic experimental study has been performed about the effect of data imbalance and other factors on support vector classification machines.