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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Classification of Binary Imbalanced Data Using A Bayesian Ensemble of Bayesian Neural Networks
verfasst von : Marcelino Lázaro, Francisco Herrera, Aníbal R. Figueiras-Vidal
Erschienen in: Engineering Applications of Neural Networks
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This paper presents a new method to deal with classification of imbalanced data. A Bayesian ensemble of neural network classifiers is proposed. Several individual neural classifiers are trained to minimize a Bayesian cost function with different decision costs, thus working at different points of the Receiver Operating Characteristic (ROC). Decisions of the set of individual neural classifiers are fused using a Bayesian rule that introduces a “balancing” parameter allowing to compensate the imbalance of available data.