The nature of statistical learning theory
The nature of statistical learning theory
Neural networks for pattern recognition
Neural networks for pattern recognition
On a constrained optimal rule for classification with unknown prior individual group membership
Journal of Multivariate Analysis
Optimization: algorithms and consistent approximations
Optimization: algorithms and consistent approximations
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
The impact of changing populations on classifier performance
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Robust Classification for Imprecise Environments
Machine Learning
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Neural Network Classification and Prior Class Probabilities
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
The Minimax Strategy for Gaussian Density Estimation. pp
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Minimax Regret Classifier for Imprecise Class Distributions
The Journal of Machine Learning Research
Local decision bagging of binary neural classifiers
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
Optimization of symbolic feature extraction for pattern classification
Signal Processing
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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.