C4.5: programs for machine learning
C4.5: programs for machine learning
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
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
International Journal of Intelligent Systems
An Evaluation of the Robustness of MTS for Imbalanced Data
IEEE Transactions on Knowledge and Data Engineering
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
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Soft Computing - A Fusion of Foundations, Methodologies and Applications
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Rule Weight Specification in Fuzzy Rule-Based Classification Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Fuzzy Logic, Soft Computing, and Applications
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
An interval type-2 fuzzy logic system for the modeling and prediction of financial applications
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
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In this contribution, we study the influence of an Evolutionary Adaptive Inference System with parametric conjunction operators for Fuzzy Rule Based Classification Systems. Specifically, we work in the context of highly imbalanced data-sets, which is a common scenario in real applications, since the number of examples that represents one of the classes of the data-set (usually the concept of interest) is usually much lower than that of the other classes. Our experimental study shows empirically that the use of the parametric conjunction operators enables simple Fuzzy Rule Based Classification Systems to enhance their performance for data-sets with a high imbalance ratio.