Abstract
Multiple classification rules are simultaneously identified by applying the Cross-Entropy method to the maximization of accuracy measures in a supervised learning context. Optimal ensembles of rules are searched through stochastic traversals of the rule space. Each rule contributes to classify a given instance when the observed attribute values belong to specific subsets of the corresponding attribute domains. Classifications of the various rules are combined applying majority voting schemes. The performance of the proposed algorithm has been tested on some data sets from the UCI repository.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kohavi, R., Wolpert, D.H.: Bias plus variance decomposition for zero-one loss functions. In: Saitta, L. (ed.) Machine Learning: Proceedings of the Thirteenth International Conference, Morgan Kaufmann, pp. 275–283 (1996)
Lichman, M.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2013). http://archive.ics.uci.edu/ml
Rokach, L.: Ensemble-based classifiers. Artificial Intelligence Review 33, 1–39 (2010)
Rubinstein, R.Y.: Optimization of computer simulation models with rare events. European Journal of Operational Research 99, 89–112 (1997)
Rubinstein, R.Y.: The cross-entropy method for combinatorial and continuous optimization. Methodology and Computing in Applied Probability 2, 127–190 (1999)
Rubinstein, R.Y., Kroese, D.P.: The cross-entropy method: A unified approach to combinatorial optimization, Monte-Carlo simulation, and machine learning. Springer (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Lafratta, G. (2016). Cross-Entropy Based Ensemble Classifiers. In: Bucciarelli, E., Silvestri, M., Rodríguez González, S. (eds) Decision Economics, In Commemoration of the Birth Centennial of Herbert A. Simon 1916-2016 (Nobel Prize in Economics 1978). Advances in Intelligent Systems and Computing, vol 475. Springer, Cham. https://doi.org/10.1007/978-3-319-40111-9_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-40111-9_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-40110-2
Online ISBN: 978-3-319-40111-9
eBook Packages: EngineeringEngineering (R0)