OPTIMIZED WEIGHTED DECODING FOR ERROR-CORRECTING OUTPUT CODES

A common method to solve a multiclass classification problem is to reduce the problem to a serial binary classification problems and combine them via Error-Correcting Output Codes (ECOC). The ECOC contains three parts: coding design, decoding algorithm, and base dichotomizer. Recently, the Loss-Weighted (LW) decoding algorithm (Escalera et al., PAMI2010), which introduces a weight matrix to the Loss-Based (LB) decoding (Allwein et al., JMLR2001), achieves improved performance over traditional decoding methods. However, the weight matrix is assigned empirically. In this paper, we present a theoretical global optimization method for the weight matrix, so as to achieve the minimal training risk. Although the experimental results on real-world image, audio and text classification tasks show that the proposed decoding method only leads to slightly better performances than others in the case of discrete outputs of the dichotomizers, the proposed method provides a new screen on the decoding methods of the ECOC.