An ensemble approach of dual base learners for multi-class classification problems |
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| Affiliation: | 1. Departamento de Ingeniería Química Petrolera and Sección de Estudios de Posgrado e Investigación, Escuela Superior de Ingeniería Química e Industrias Extractivas, Instituto Politécnico Nacional, UPALM, Ed. 8, Lindavista, C.P. 07738 México, D.F., Mexico;2. Departamento de Ingeniería Química Industrial, Laboratorio de Investigación en Fisicoquímica y Materiales, Escuela Superior de Ingeniería Química e Industrias Extractivas, Instituto Politécnico Nacional, Edif. Z-5, 2° piso, UPALM, Lindavista, C.P. 07738 México, D.F., Mexico;1. Center for Statistical Research and Methodology, U.S. Census Bureau, 4600 Silver Hill Road, Washington, D.C. 20233-9100, USA;2. Department of Mathematics, University of California, San Diego, 9500 Gilman Drive, Mail Code 0112, La Jolla, CA 92093-0112, USA;1. College of Information Sciences and Technology, Donghua University, Shanghai 201620, China;2. MRC—University of Glasgow Centre for Virus Research, Glasgow G11 5JR, United Kingdom;3. Engineering Research Center of Digitized Textile & Fashion Technology, Ministry of Education, Shanghai 201620, China;1. Department of Cardio-Thoracic Surgery, VU University Medical Center, Amsterdam, The Netherlands;2. Department of Surgery, VU University Medical Center, Amsterdam, The Netherlands;3. Department of Radiation Oncology, VU University Medical Center, Amsterdam, The Netherlands;4. Department of Pulmonary Diseases, VU University Medical Center, Amsterdam, The Netherlands;5. Department of Thoracic Oncology, Netherlands Cancer Institute—Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands;6. Department of Surgery, Netherlands Cancer Institute—Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands;7. Department of Research, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands |
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| Abstract: | In this work, we formalise and evaluate an ensemble of classifiers that is designed for the resolution of multi-class problems. To achieve a good accuracy rate, the base learners are built with pairwise coupled binary and multi-class classifiers. Moreover, to reduce the computational cost of the ensemble and to improve its performance, these classifiers are trained using a specific attribute subset. This proposal offers the opportunity to capture the advantages provided by binary decomposition methods, by attribute partitioning methods, and by cooperative characteristics associated with a combination of redundant base learners. To analyse the quality of this architecture, its performance has been tested on different domains, and the results have been compared to other well-known classification methods. This experimental evaluation indicates that our model is, in most cases, as accurate as these methods, but it is much more efficient. |
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| Keywords: | Ensemble of classifiers Multi-class classification Artificial Neural Networks Feature Selection Diversity |
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