From dynamic classifier selection to dynamic ensemble selection |
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| Authors: | Albert HR Ko Robert Sabourin Alceu Souza Britto Jr |
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| Affiliation: | 1. Centro de Informática, Universidade Federal de Pernambuco, Recife, PE, Brazil;2. École de Technologie Supérieure, Université du Québec, Montreal, Quebec, Canada;1. Federal University of Parana (UFPR), Rua Cel. Francisco H. dos Santos, Curitiba, PR 100-81531-990, Brazil;2. Ponta Grossa State University, Av. General Carlos Cavalcanti, Ponta Grossa, PR, 4748-84030-900, Brazil;3. Pontifical Catholic University of Parana (PUCPR), R. Imaculada Conceição, Curitiba, PR\n1155-80215-901, Brazil;4. École de Technologie Supérieure, 1100 Notre-Dame Street West, Montreal, Quebec, Canada;1. Department of Computer Science, Virginia Commonwealth University, USA;2. Departament of Automática y Computación, Universidad Pública de Navarra, Pamplona, Spain;3. Department of Systems and Computer Networks, Faculty of Electronics, Wroc?aw University of Technology, Wroc?aw, Poland;4. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain;5. Faculty of Computing and Information Technology - North Jeddah, King Abdulaziz University, Jeddah, 21589, Saudi Arabia;1. Department of Computer Science and Artificial Intelligence, University of Granada, Granada 18071, Spain;2. School of Management, Hangzhou Dianzi University, Hangzhou 310018, China;3. Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia;4. Faculty of Computing and Information Technology, University of Jeddah, Jeddah 21589, Saudi Arabia |
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| Abstract: | In handwritten pattern recognition, the multiple classifier system has been shown to be useful for improving recognition rates. One of the most important tasks in optimizing a multiple classifier system is to select a group of adequate classifiers, known as an Ensemble of Classifiers (EoC), from a pool of classifiers. Static selection schemes select an EoC for all test patterns, and dynamic selection schemes select different classifiers for different test patterns. Nevertheless, it has been shown that traditional dynamic selection performs no better than static selection. We propose four new dynamic selection schemes which explore the properties of the oracle concept. Our results suggest that the proposed schemes, using the majority voting rule for combining classifiers, perform better than the static selection method. |
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