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Stochastic Organization of Output Codes in Multiclass Learning Problems

Institute for Network Theory and Signal Processing, Munich University of Technology, D–80290 Munich, Germany

Werner Weichselberger

Institute for Network Theory and Signal Processing, Munich University of Technology, D–80290 Munich, Germany

The best-known decomposition schemes of multiclass learning problems are one per class coding (OPC) and error-correcting output coding (ECOC). Both methods perform a prior decomposition, that is, before training of the classifier takes place. The impact of output codes on the inferred decision rules can be experienced only after learning. Therefore, we present a novel algorithm for the code design of multiclass learning problems. This algorithm applies a maximum-likelihood objective function in conjunction with the expectation-maximization (EM) algorithm. Minimizing the augmented objective function yields the optimal decomposition of the multiclass learning problem in two-class problems. Experimental results show the potential gain of the optimized output codes over OPC or ECOC methods.