A unified framework of binary classifiers ensemble for multi-class classification
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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A聽multi-class classifier based on the Bradley-Terry model predicts the multi-class label of an input by combining the outputs from multiple binary classifiers, where the combination should be a priori designed as a code word matrix. The code word matrix was originally designed to consist of +1 and 驴1 codes, and was later extended into deal with ternary code {+1,0,驴1}, that is, allowing 0 codes. This extension has seemed to work effectively but, in fact, contains a problem: a binary classifier forcibly categorizes examples with 0 codes into either +1 or 驴1, but this forcible decision makes the prediction of the multi-class label obscure. In this article, we propose a Boosting algorithm that deals with three categories by allowing a `don't care' category corresponding to 0 codes, and present a modified decoding method called a `ternary' Bradley-Terry model. In addition, we propose a couple of fast decoding schemes that reduce the heavy computation by the existing Bradley-Terry model-based decoding.