Multiclass SVM active learning algorithm based on decision directed acyclic graph and one versus one

Abstract

The classical training algorithms of support vector machines (SVM) are supervised learning algorithms which based on large-scale labeled samples, while these labeled samples are not easy to be acquired or labeled costly and class–unbalanced dataset, meanwhile these SVM algorithms are originally designed for the solution of two-class problems. To solve these problems of SVM, An Active learning algorithm based on decision directed acyclic graph (DDAG) for SVM is proposed in the paper, which train the multiclass SVMs using as few labeled instances as possible while maintaining the same SVM performance, or achieving the generalization performance of SVM classification as good as possible. The experimental results on the UCI data show that the proposed approach can achieve higher clasification accuracy, but using less labeled samples, while improving generalization performance and ruducing the marking costs of SVM training.