关键词: 支持向量机, 主动学习, 向量余弦, 冗余度, 平衡度

Abstract: This paper proposes a support vector machine (SVM) active learning strategy based on vector cosine for the high dimensional dataset to solve the problem that the traditional support vector machine based on active learning can not measure the correlation degree of different samples by Euclidean distance and obtains the low generalization ability, namely COS_SVMactive method. By measuring the information redundancy of training samples based on vector cosine on active learning procedure, several the most valuable samples are selected and need be labeled by experts. In each samples labeling loop, the balance of labeled data is gradually adjusted in order to achieve good generalization performance. The experimental results demonstrate that, compared with common SVM active learning based on random sampling (RS_SVMactive) and SVM active learning based on distance (DIS_SVMactive) methods, the proposed COS_SVMactive method can not only improve classification accuracy, but also reduce the artificial labeling cost.

Key words: support vector machine, active learning, vector cosine, redundancy, balance