On the Decomposition of Polychotomies into Dichotomies
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
The Journal of Machine Learning Research
An Empirical Study for the Multi-class Imbalance Problem with Neural Networks
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Pattern Analysis & Applications
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data & Knowledge Engineering
Aggregation of classifiers for staining pattern recognition in antinuclear autoantibodies analysis
IEEE Transactions on Information Technology in Biomedicine
On rejecting unreliably classified patterns
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Decomposition Methods and Learning Approaches for Imbalanced Dataset: An Experimental Integration
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
A multi-objective optimisation approach for class imbalance learning
Pattern Recognition
Automatic facial expression recognition using statistical-like moments
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
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A key issue in machine learning is the ability to cope with recognition problems where one or more classes are under-represented with respect to the others. Indeed, traditional algorithms fail under class imbalanced distribution resulting in low predictive accuracy over the minority classes. While large literature exists on binary imbalanced tasks, few researches exist for multiclass learning. In this respect, we present here a new method for imbalanced multiclass learning within the One-per-Class decomposition framework. Once the multiclass task is divided into several binary tasks, the proposed reconstruction rule discriminates between safe and dangerous classifications. Then, it sets the multiclass label using information on both data distributions and classification reliabilities provided by each binary classifier, lowering the effects of class skew and improving the performance. We favorably compare the proposed reconstruction rule with the standard One-per-Class method on ten datasets using four classifiers.