Thinned-ECOC ensemble based on sequential code shrinking

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.

► A heuristic method for application-dependent design of optimal ECOC is proposed.
► Thinned-ECOC efficiently removes unnecessary columns of any initial code matrix.
► Computational cost of the ensemble is reduced while preserving its accuracy.
► Proposed method has been validated on the UCI machine learning datasets.
► Thinned-ECOC is applied on the face recognition and cancer classification problems.