Fuzzy integral for a rapid mixture of support vector machines

In the last recent years, there was an important interest in using mixture of different support vector machines (SVMs) for large scale data bases. This approach aims to reduce the complexity of the training algorithm of SVMs which is at least quadratic to the number of training data. Commonly, this objective is reached by dividing the data set into small subsets which are much easy to learn. In this paper, we present a new approach for mixture of SVMs based on the notion of fuzzy integral. In this method, a fuzzy measure is used as a gater to avoid the time complexity caused by conventional gaters such as neural networks. Experiments were conducted on USPS handwritten digits data base whose learning is a challenging task. The results obtained indicate that the proposed scheme improves significantly the training time while keeping accuracy at least as good as the accuracy of a single SVM.