Efficient data selection approach in projected feature space for fast training support vector machines
Sonia Chaibi, Mohamed Tayeb Laskri · International Journal of Business Intelligence and Data Mining · 2014
Support vector machines (SVMs) have shown superior performance compared to other machine learning techniques, especially in classification problems. Yet one limitation is the long computational training time which increases with the data size. This problem has been investigated thoroughly and different algorithms for classification have been used with various success rates. Among them, clustering techniques have shown a considerable success to reduce SVM's data training. However, once these solutions are used for large scale datasets it becomes clear that using only clustering approaches is insufficient. In this paper, we tackle the problem of how to combine clustering methods and feature reducing techniques to minimise efficiently SVM's complexity. Several experiments on different datasets show that the proposed solution can be a promised way for fast training SVMs on large scale datasets.