Minimax classifiers based on neural networks

The problem of designing a classifier when prior probabilities are not known or are not representative of the underlying data distribution is discussed in this paper. Traditional learning approaches based on the assumption that class priors are stationary lead to sub-optimal solutions if there is a...

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Bibliographic Details
Published in:Pattern recognition 2005, Vol.38 (1), p.29-39
Main Authors: Alaiz-Rodríguez, Rocío, Guerrero-Curieses, Alicia, Cid-Sueiro, Jesús
Format: Article
Language:English
Subjects:
Minimax decision rules
Neural networks
Pattern classification
Uncertainty in priors
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Summary:The problem of designing a classifier when prior probabilities are not known or are not representative of the underlying data distribution is discussed in this paper. Traditional learning approaches based on the assumption that class priors are stationary lead to sub-optimal solutions if there is a mismatch between training and future (real) priors. To protect against this uncertainty, a minimax approach may be desirable. We address the problem of designing a neural-based minimax classifier and propose two different algorithms: a learning rate scaling algorithm and a gradient-based algorithm. Experimental results show that both succeed in finding the minimax solution and it is also pointed out the differences between common approaches to cope with this uncertainty in priors and the minimax classifier.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2004.05.007