A robust ensemble learning using zero–one loss function

A robust ensemble learning using zero–one loss function

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Article ID: iaor20091302
Country: Japan
Volume: 51
Issue: 1
Start Page Number: 95
End Page Number: 110
Publication Date: Mar 2008
Journal: Journal of the Operations Research Society of Japan
Authors: , ,
Keywords: gradient methods, neural networks, statistics: inference, datamining
Abstract:

Classifier is used for pattern recognition in various fields including data mining. Boosting is an ensemble learning method to boost (enhance) an accuracy of single classifier. We propose a new, robust boosting method by using a zero–one step function as a loss function. In deriving the method, the MarginBoost technique is blended with the stochastic gradient approximation algorithm, called Stochastic Noise Reaction (SNR). Based on intensive numerical experiments, we show that the proposed method is actually better than AdaBoost on test error rates in the case of noisy, mislabeled situation.

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