A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Neural Computation
C4.5: programs for machine learning
C4.5: programs for machine learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Rectangular basis functions applied to imbalanced datasets
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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In this paper a novel classification method for real world classification tasks is proposed. The method was designed to overcome the difficulties encountered by traditional methods when coping with those real world problems where the key issue is the detection of particular situations - such as for instance machine faults or anomalies - which in some frameworks are hard to be recognized due to some interacting factors that are analyzed within the paper. The method is described and tested on two industrial problems, which show the goodness of the proposed approach and encourage its use in the industrial environments.