Fuzzy integral to speed up support vector machines training for pattern classification

Article type: Research Article

Authors: Nemmour, Hassiba^{; *} | Chibani, Youcef

Affiliations: Signal Processing Laboratory, Faculty of Electronic and Computer Sciences, University of Sciences and Technology Houari Boumediene, EL-Alia B. P. 32, 16111, Algiers, Algeria

Correspondence: [*] Corresponding author. E-mail: [email protected]

Abstract: The major drawback of Support Vector Machines (SVMs) consists of the training time, which is at least quadratic to the number of data. Among the multitude of approaches developed to alleviate this limitation, several research works showed that mixtures of experts can drastically reduce the runtime of SVMs. The mixture employs a set of SVMs each of which is trained on a sub-set of the original dataset while the final decision is evaluated throughout a gater. The present work proposes a new support vector mixture in which Sugeno's fuzzy integral is used as a gater to remove the time complexity induced by conventional gaters such as artificial neural networks. Experiments conducted on standard datasets of optical character and face recognition reveal that the proposed approach gives a significant reduction of the runtime while keeping at least the same accuracy as the SVM trained over the whole dataset.

Keywords: Support vector machines, pattern recognition, mixture, fuzzy integral, fuzzy measure

DOI: 10.3233/KES-2010-0196

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 14, no. 3, pp. 127-138, 2010