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Abstract

A long-standing goal of machine learning is to build a system which can detect a large number of classes with accuracy and efficiency. Some relationships between classes would become a scale-free network in which we can classify the assigned class very fast. Many available methods for multiclass problems have been proposed in the literatures, such as AdaBoost.ECC [4], AdaBoost.ERP, [7] and JointBoost [12]. However, many of them are inaccurate or time-consuming on training. In this paper, we propose a new algorithm, called AdaBoost.ERC, which combines the approach of Dietterich and Bakiri [2] based on error correcting output codes (ECOC) and Shapire’s boosting algorithm [3] [10]. With advantages of both concepts, our new approach achieves better performance compared to AdaBoost.ECC, AdaBoost.ERP, and JointBoost.

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