Abstract:

To the problem that large-scale labeled samples is not easy to acquire and the class-unbalanced dataset in the course of souport vector machine (SVM) training, an active learning algorithm based on query by committee (QBC) for SVM(QBC-ASVM) is proposed,which efficiently combines the improved QBC active learning and the weighted SVM.In this method,QBC active learning is used to select the samples which are the most valuable to the current SVM classifier,and the weighted SVM is used to reduce the impact of the unba-lanced-data set on SVMs active learning. The experimental results show that the proposed approach can considerably reduce the labeled samples and costs compared with the passive SVM, and at the same time, it can ensure that the accurate classification performance is kept as the passive SVM, and the proposed method improves generalization performance and also expedites the SVM training.