Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
The Journal of Machine Learning Research
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Learning valued preference structures for solving classification problems
Fuzzy Sets and Systems
Evolutionary rule-based systems for imbalanced data sets
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Information Sciences: an International Journal
Hybridization of fuzzy GBML approaches for pattern classification problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Polichotomies on imbalanced domains by one-per-class compensated reconstruction rule
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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In a classification task, the imbalance class problem is present when the data-set has a very different distribution of examples among their classes. The main handicap of this type of problem is that standard learning algorithms consider a balanced training set and this supposes a bias towards the majority classes. In order to provide a correct identification of the different classes of the problem, we propose a methodology based on two steps: first we will use the one-vs-one binarization technique for decomposing the original data-set into binary classification problems. Then, whenever each one of these binary subproblems is imbalanced, we will apply an oversampling step, using the SMOTE algorithm, in order to rebalance the data before the pairwise learning process. For our experimental study we take as basis algorithm a linguistic Fuzzy Rule Based Classification System, and we aim to show not only the improvement in performance achieved with our methodology against the basic approach, but also to show the good synergy of the pairwise learning proposal with the selected oversampling technique.