Abstract: In transductive support vector machine, sample labeling error will result in error propagation in the iterative process. It affects the accuracy of sample labeling in the next iteration and makes mistakes constantly being accumulated. Eventually leading to classification hyperplane offset. Under imbalanced dataset, there is higher classification error rate of traditional SVM that causes the labeling error rate in each iterative for TSVM. Therefore, the algorithm of TSVM for imbalanced dataset is proposed in this paper. We dynamic calculates the penalty factor of every class according to the relationship of sample density of every class to improve the accuracy of labeling sample in each iterative. The algorithm inherits its rules of progressive labeling and dynamic adjusting, and reduces the offset of the classification hyperplane. Finally, experiment results with KDD CUP99 dataset show the algorithm can improve the classification performance at imbalanced dataset, especially for the minority class samples.

Key words: support vector machine, transductive learning, semi-supervised learning, imbalanced dataset, intrusion detection