Abstract—In the past decade, many researchers have employed various methodologies to combine decisions of multiple classifiers in order to achieve high pattern recognition performance. However, two main strategies of combination are possible. The first strategy uses the different opinions of classifiers to make the final decision; it corresponds to classifiers fusion. The second strategy uses the decisions of one or more better classifiers in a specific region of feature space; it corresponds to the selection of classifiers. In this paper, we propose a dynamic multiple classifiers selection system organized in two levels of decision. Two classification methods are used: Semi-Supervised Fuzzy Pattern Matching (SSFPM) and Support Vector Machines (SVM). SSFPM is used to determine the ambiguous regions. Then, the patterns located in these regions are classified by SVM. The detection of the occurrence of new classes and the learning of their membership functions are achieved online using SSFPM. This combination helps to overcome the drawbacks of the both methods by gathering their advantages leading to increase the classification performance.
Index Terms—Classifiers Selection, Multiple Classifier Systems, Pattern Recognition, Semi-Supervised Fuzzy Pattern Matching, Support Vector Machine
Manuscript received March 23, 2011. All authors are with Université de Reims Champagne-Ardenne, Centre de Recherche en STIC (URCA-CReSTIC) Moulin de la Housse, BP 1039, 51687Reims Cedex, France {omar.ayad,moamar.sayed-mouchaweh}@univ-reims.fr
Cite: O. Ayad and M. Syed-Mouchaweh, "Multiple Classifiers Approach based on Dynamic Selection to Maximize Classification Performance," International Journal of Machine Learning and Computing vol. 1, no. 2, pp. 154-162, 2011.