Fast multi-label SVM training based on approximate extreme points
Zhongwei Sun, Zhongwen Guo, Chao Liu, Mingxing Jiang, Xi Wang · Intelligent Data Analysis · 2018
Under the framework of multi-label classification, the excessive training time restricts the availability of non-linear kernel SVM (Support Vector Machine) classification algorithm on large-scale data sets. To solve this problem, this paper provides a fast multi-label SVM classification algorithm b ased on approximate extreme points (AEMLSVM). Firstly, it utilizes the approximate extreme point technique to obtain representative sets from the training data set. These representative sets not only retain almost all information of the training data set, but also its size is much smaller than that of training data set. After that, SVM is trained on the representative sets. Furthermore, the improved AEMLSVM algorithm (AEMLSVM-DEC) adopts DEC (Different Error Costs) technique to solve the label data imbalanced problem. We have conducted extensive experiments on four large-scale benchmark data sets. The results show that the proposed algorithms can effectively reduce training time, and their classification performance is similar to that of the traditional multi-label SVM algorithm. They outperform other scalable multi-label SVM algorithms in training time and classification performance. By adopting DEC method to solve the label data imbalanced problem, the AEMLSVM-DEC algorithm has a better classification performance than AEMLSVM algorithm.