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.
This work has been partially supported by Research Grant S2013/ICE-2845 (CASI-CAM-CM), DGUI - Comunidad de Madrid
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© 2015 Springer International Publishing Switzerland
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Lázaro, M., Herrera, F., Figueiras-Vidal, A.R. (2015). Classification of Binary Imbalanced Data Using A Bayesian Ensemble of Bayesian Neural Networks. In: Iliadis, L., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2015. Communications in Computer and Information Science, vol 517. Springer, Cham. https://doi.org/10.1007/978-3-319-23983-5_28
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DOI: https://doi.org/10.1007/978-3-319-23983-5_28
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