Thinned-ECOC ensemble based on sequential code shrinking |
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Authors: | Nima Hatami |
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Affiliation: | DIEE - Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, I-09123 Cagliari, Italy |
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Abstract: | Error-correcting output coding (ECOC) is a strategy to create classifier ensembles which reduces a multi-class problem into some binary sub-problems. A key issue in designing any ECOC classifier refers to defining optimal codematrix having maximum discrimination power and minimum number of columns. This paper proposes a heuristic method for application-dependent design of optimal ECOC matrix based on a thinning algorithm. The main idea of the proposed Thinned-ECOC method is to successively remove some redundant and unnecessary columns of any initial codematrix based on a metric defined for each column. As a result, computational cost of the ensemble is reduced while preserving its accuracy. Proposed method has been validated using the UCI machine learning database and further applied to a couple of real-world pattern recognition problems (the face recognition and gene expression based cancer classification). Experimental results emphasize the robustness of Thinned-ECOC in comparison with existing state-of-the-art code generation methods. |
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Keywords: | Multiple classifier systems (MCS) Thinning ensemble Error-correcting output coding (ECOC) Multi-class classification Face recognition Gene expression classification |
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