Instance-Based Learning Algorithms
Machine Learning
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Increasing sensitivity of preterm birth by changing rule strengths
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Improving Minority Class Prediction Using Case-Specific Feature Weights
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Improved Rooftop Detection in Aerial Images with Machine Learning
Machine Learning
A robust minimax approach to classification
The Journal of Machine Learning Research
Learning large margin classifiers locally and globally
ICML '04 Proceedings of the twenty-first international conference on Machine learning
The Minimum Error Minimax Probability Machine
The Journal of Machine Learning Research
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Using text classifiers for numerical classification
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Learning classifiers from imbalanced data based on biased minimax probability machine
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Imbalanced learning with a biased minimax probability machine
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Machine learning for medical diagnosis: history, state of the art and perspective
Artificial Intelligence in Medicine
Model selection for a medical diagnostic decision support system: a breast cancer detection case
Artificial Intelligence in Medicine
Information Sciences: an International Journal
Analysis of an evolutionary RBFN design algorithm, CO2RBFN, for imbalanced data sets
Pattern Recognition Letters
Linguistic cost-sensitive learning of genetic fuzzy classifiers for imprecise data
International Journal of Approximate Reasoning
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
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The Biased Minimax Probability Machine (BMPM) constructs a classifier which deals with the imbalanced learning tasks. It provides a worst-case bound on the probability of misclassification of future data points based on reliable estimates of means and covariance matrices of the classes from the training data samples, and achieves promising performance. In this paper, we develop a novel yet critical extension training algorithm for BMPM that is based on Second-Order Cone Programming (SOCP). Moreover, we apply the biased classification model to medical diagnosis problems to demonstrate its usefulness. By removing some crucial assumptions in the original solution to this model, we make the new method more accurate and robust. We outline the theoretical derivatives of the biased classification model, and reformulate it into an SOCP problem which could be efficiently solved with global optima guarantee. We evaluate our proposed SOCP-based BMPM (BMPM"S"O"C"P) scheme in comparison with traditional solutions on medical diagnosis tasks where the objectives are to focus on improving the sensitivity (the accuracy of the more important class, say ''ill'' samples) instead of the overall accuracy of the classification. Empirical results have shown that our method is more effective and robust to handle imbalanced classification problems than traditional classification approaches, and the original Fractional Programming-based BMPM (BMPM"F"P).