Research and Application of Fast Multi-label SVM Classification Algorithm Using Approximate Extreme Points

Abstract

In Large-Scale of Multi-label classification framework, applications of Non-linear kernel support vector machines (SVMs) classification algorithm are restricted by the problem of excessive training time. Hence, we propose Approximate Extreme Points Multi-label Support Vector Machine (AEMLSVM) classification algorithm to solve this problem. The first step of AEMLSVM classification algorithm is using approximate extreme points method to extract the training subsets, called the representative sets, from training dataset. Then SVM is trained from the representative sets. In addition, the AEMLSVM classification algorithm also can adopt Cost-Sensitive method to deal with the imbalanced data issue. Experiment results from three Large-Scale public datasets show that AEMLSVM classification algorithm can substantially shorten training time greatly and obtain a similar result compared with the traditional Multi-label SVM classification algorithm. It also exceeds existing fast Multi-label SVM classification algorithm in both training time and effectiveness. Besides, AEMLSVM classification algorithm has advantages in the classification time.