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Statistical approaches to combining binary classifiers for multi-class classification
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DOI：10.1016/j.neucom.2010.09.004.png)
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Abstract
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One of the popular methods for multi-class classification is to combine binary classifiers. In this paper, we propose a new approach for combining binary classifiers. Our method trains a combining method of binary classifiers using statistical techniques such as penalized logistic regression, stacking, and a sparsity promoting penalty. Our approach has several advantages. Firstly, our method outperforms existing methods even if the base classifiers are well-tuned. Secondly, an estimate of conditional probability for each class can be naturally obtained. Furthermore, we propose selecting relevant binary classifiers by adding the group lasso type penalty in training the combining method. (C) 2010 Elsevier B.V. All rights reserved.
Keywords:
Multi-class classification
Combining binary classifiers
Meta-learning
Stacking
Group lasso
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