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
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification, 2nd edn. John Wiley & Sons (2001)
Widrow, B., Rumelhard, D.E., Lehr, M.A.: Neural networks: Applications in industry, business and science. Communications of the ACM 37(3), 93–105 (1994)
Zhang, G.P.: Neural networks for classification: A survey. IEEE Transactions on Systems, Man, and Cybernetics 30(4), 451–462 (2000)
He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering 21(9), 1263–1284 (2009)
Galar, M., Fernández, A., Barrenechea, E., Herrera, F.: EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling. Pattern Recognition 46, 3460–3471 (2013)
Galar, M., Fernández, A., Barrenechea, E., Bustince, H., Herrera, F.: A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews 42(4), 463–484 (2012)
Kay, S.M.: Fundamentals of statistical signal processing: detection theory. Prentice-Hall Inc., Upper Saddle River (1998)
Scharf, L.S.: Statistical signal processing: detection, estimation, and time series analysis. Addison-Wesley (1991)
Van Trees, H.L.: Detection, Estimation, and Modulation Theory: Part I. John Wiley and Sons, New York (1968)
Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27(8), 861–874 (2006)
Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30(7), 1145–1159 (1997)
Cid-Sueiro, J., Arribas, J.I., Urbán-Muñoz, S., Figueiras-Vidal, A.R.: Cost functions to estimate a posteriori probabilities in multiclass problems. IEEE Transactions on Neural Networks 10(3), 645–656 (1999)
Parzen, E.: On the estimation of a probability density function and the mode. Annals of Mathematical Statistics 33, 1065–76 (1962)
Alcalá-Fdez, A., Fernández, A., Luengo, J., Derrac, J., García, S., Sánchez, L., Herrera, F.: KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. Journal of Multiple-Valued Logic and Soft Computing 17(2–3), 255–287 (2011)
Rumelhart, D.E., Hinton, G.E., Willians, R.J.: Learning representations by back-propagating errors. Nature (London) 323, 533–536 (1986)
Widrow, B., Lehr, M.A.: 30 years of adaptive neural networks: perceptron, madaline and backpropagation. Proceedings of the IEEE 78(9), 1415–1441 (1990)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-23983-5_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23981-1
Online ISBN: 978-3-319-23983-5
eBook Packages: Computer ScienceComputer Science (R0)