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
Explicitly representing expected cost: an alternative to ROC representation
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Robust Classification for Imprecise Environments
Machine Learning
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Neural Network Classification and Prior Class Probabilities
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
A Study on the Effect of Class Distribution Using Cost-Sensitive Learning
DS '02 Proceedings of the 5th International Conference on Discovery Science
The Minimax Strategy for Gaussian Density Estimation. pp
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Transforming classifier scores into accurate multiclass probability estimates
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An iterative method for multi-class cost-sensitive learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting good probabilities with supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
The class imbalance problem: A systematic study
Intelligent Data Analysis
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Minimax classifiers based on neural networks
Pattern Recognition
A competitive minimax approach to robust estimation of random parameters
IEEE Transactions on Signal Processing
Linear minimax regret estimation of deterministic parameters with bounded data uncertainties
IEEE Transactions on Signal Processing
A fixed-point algorithm to minimax learning with neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Diagnosis of gastric carcinoma by classification on feature projections
Artificial Intelligence in Medicine
Universal composite hypothesis testing: a competitive minimax approach
IEEE Transactions on Information Theory
On the structure of strict sense Bayesian cost functions and its applications
IEEE Transactions on Neural Networks
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Quantifying the proportion of damaged sperm cells based on image analysis and neural networks
SMO'08 Proceedings of the 8th conference on Simulation, modelling and optimization
Quantification and semi-supervised classification methods for handling changes in class distribution
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving Classification under Changes in Class and Within-Class Distributions
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Assessing the impact of changing environments on classifier performance
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
A unifying view on dataset shift in classification
Pattern Recognition
Robustness of classifiers to changing environments
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Handling concept drift via ensemble and class distribution estimation technique
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Scientific and Technical Information Processing
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The design of a minimum risk classifier based on data usually stems from the stationarity assumption that the conditions during training and test are the same: the misclassification costs assumed during training must be in agreement with real costs, and the same statistical process must have generated both training and test data. Unfortunately, in real world applications, these assumptions may not hold. This paper deals with the problem of training a classifier when prior probabilities cannot be reliably induced from training data. Some strategies based on optimizing the worst possible case (conventional minimax) have been proposed previously in the literature, but they may achieve a robust classification at the expense of a severe performance degradation. In this paper we propose a minimax regret (minimax deviation) approach, that seeks to minimize the maximum deviation from the performance of the optimal risk classifier. A neural-based minimax regret classifier for general multi-class decision problems is presented. Experimental results show its robustness and the advantages in relation to other approaches.