Abstract
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.
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