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ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
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Rectangular basis functions applied to imbalanced datasets
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Boosting prediction accuracy on imbalanced datasets with SVM ensembles
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IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Artificial Intelligence in Medicine
Use of a quasi-Newton method in a feedforward neural network construction algorithm
IEEE Transactions on Neural Networks
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Abstract: This paper describes a novel binary classification method named LASCUS that can be applied to uneven datasets and sensitive problems such as malfunction detection. Such method aims at filling the gap left by traditional algorithms which have difficulties when coping with unbalanced datasets and are not able to satisfactorily recognize unfrequent patterns. The proposed method is based on the use of a self organizing map (SOM) and of a fuzzy inference system (FIS). The SOM creates a set of clusters to be associated either to frequent or unfrequent situations while the FIS determines such association on the basis of data distribution. The method has been tested on the widely used benchmarking Wisconsin breast cancer database and on two industrial applications. The obtained results, which are discussed in the paper, are encouraging and in line with expectations.