Weight optimization and layered clustering-based ECOC

Error correcting output code (ECOC) is a general framework of solving a multiclass classification problem via a binary-class classifier ensemble. In this paper, we propose a new heuristic coding method, named weight optimization and layered clustering-based ECOC (WOLC-ECOC). It iterates the following two steps until the training risk converges. The first step employs the layered clustering-based approach [1]. The approach can construct multiple different strong binary-class classifiers on a given binary-class problem, so that the heuristic training process will not be blocked by some difficult binary-class problems. The second step is the weight optimization technique [2]. It guarantees the non-increasing of the heuristic training process whenever we add new classifiers to the ECOC ensemble. Experimental results on several benchmark sets demonstrate that WOLC-ECOC is more effective than 15 referenced coding-decoding ECOC pairs.