SVM optimization algorithm based on dynamic clustering and ensemble learning for large scale dataset
Shiyu Shu, Lihong Ren, Yongsheng Ding, Kuangrong Hao, Rui Jiang · 2014
This paper studies on the predicted regression model of support vector machines (SVM). Aiming at the shortage that with the amount of samples grows, training time increases rapidly as well, we propose an optimization algorithm to optimize it for large scale dataset. The optimization algorithm is based on ensemble learning and dynamic clustering. Firstly, we use dynamic cluster method to generate different types of sub training set based on fuzzy granular. Then we construct SVM sub-learners. Afterwards we synthesize outputs of each sub-learner by using the strategy of mean squared error. Simulation experimental results demonstrate that the optimization algorithm can increase training speed obviously, and keep the original accuracy compared to traditional SVM.