Information Sciences: an International Journal
Predictive maintenance with multi-target classification models
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
International Journal of Approximate Reasoning
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Multi-objective evolutionary fuzzy systems
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
Expert Systems with Applications: An International Journal
A measure oriented training scheme for imbalanced classification problems
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Efficient classifiers for multi-class classification problems
Decision Support Systems
An efficient multi-objective evolutionary fuzzy system for regression problems
International Journal of Approximate Reasoning
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We exploit an evolutionary three-objective optimization algorithm to produce a Pareto front approximation composed of fuzzy rule-based classifiers (FRBCs) with different trade-offs between accuracy (expressed in terms of sensitivity and specificity) and complexity (computed as sum of the conditions in the antecedents of the classifier rules). Then, we use the ROC convex hull method to select the potentially optimal classifiers in the projection of the Pareto front approximation onto the ROC plane. Our method was tested on 13 highly imbalanced datasets and compared with 2 two-objective evolutionary approaches and one heuristic approach to FRBC generation, and with three well-known classifiers. We show by the Wilcoxon signed-rank test that our three-objective optimization approach outperforms all the other techniques, except for one classifier, in terms of the area under the ROC convex hull, an accuracy measure used to globally compare different classification approaches. Further, all the FRBCs in the ROC convex hull are characterized by a low value of complexity. Finally, we discuss how, the misclassification costs and the class distributions are fixed, we can select the most suitable classifier for the specific application. We show that the FRBC selected from the convex hull produced by our three-objective optimization approach achieves the lowest classification cost among the techniques used as comparison in two specific medical applications.