Reduction of training datasets via fuzzy entropy for support vector machines

Support vector machines (SVM) are currently the state-of-the-art models for many classification problems but they suffer from the complexity of their training algorithm that is at least quadratic with respect to the number of examples. Hence, it is hard to try to solve real-life problems with more than a few hundreds of thousands examples by SVM. The present paper proposes a new heuristic method based on the fuzzy entropy. Under the circumstances that there are little support vectors in the original training set, this new method can effectively preselect the boundary subset which contain overwhelming majority support vectors. By substituting the boundary subset for original training set, our method greatly reduces the training time, while the ability of support vector machine to classification is unaffected. Comparing to other analogous methods, the merit of our method is that there are no parameters for determining the border of subset. The preliminary experimental results indicate that our approach is efficient and practical.