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
Increasing sensitivity of preterm birth by changing rule strengths
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Improved Rooftop Detection in Aerial Images with Machine Learning
Machine Learning
A robust minimax approach to classification
The Journal of Machine Learning Research
Linear Asymmetric Classifier for cascade detectors
ICML '05 Proceedings of the 22nd international conference on Machine learning
A comparative study of Minimax Probability Machine-based approaches for face recognition
Pattern Recognition Letters
An Evaluation of the Robustness of MTS for Imbalanced Data
IEEE Transactions on Knowledge and Data Engineering
An information granulation based data mining approach for classifying imbalanced data
Information Sciences: an International Journal
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Using granular computing model to induce scheduling knowledge in dynamic manufacturing environments
International Journal of Computer Integrated Manufacturing
Local reweight wrapper for the problem of imbalance
International Journal of Artificial Intelligence and Soft Computing
Exploratory undersampling for class-imbalance learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An unsupervised self-organizing learning with support vector ranking for imbalanced datasets
Expert Systems with Applications: An International Journal
Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Computational Biology and Chemistry
Improved response modeling based on clustering, under-sampling, and ensemble
Expert Systems with Applications: An International Journal
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Preprocessing unbalanced data using support vector machine
Decision Support Systems
Expert Systems with Applications: An International Journal
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We consider the problem of the binary classification on imbalanced data, in which nearly all the instances are labelled as one class, while far fewer instances are labelled as the other class, usually the more important class. Traditional machine learning methods seeking an accurate performance over a full range of instances are not suitable to deal with this problem, since they tend to classify all the data into the majority, usually the less important class. Moreover, some current methods have tried to utilize some intermediate factors, e.g., the distribution of the training set, the decision thresholds or the cost matrices, to influence the bias of the classification. However, it remains uncertain whether these methods can improve the performance in a systematic way. In this paper, we propose a novel model named Biased Minimax Probability Machine. Different from previous methods, this model directly controls the worst-case real accuracy of classification of the future data to build up biased classifiers. Hence, it provides a rigorous treatment on imbalanced data. The experimental results on the novel model comparing with those of three competitive methods, i.e., the Naive Bayesian classifier, the k-Nearest Neighbor method, and the decision tree method C4.5, demonstrate the superiority of our novel model.