Abstract
Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier called naïve Bayes is competitive with state of the art classifiers. This simple approach stands from assumptions of conditional independence among features given the class. In this paper a new naïve Bayes classifier called Interval Estimation naïve Bayes is proposed. Interval Estimation naïve Bayes is performed in two phases. First, an interval estimation of each probability necessary to specify the naïve Bayes is calculated. On the second phase the best combination of values inside these intervals is calculated using a heuristic search that is guided by the accuracy of the classifiers. The founded values in the search are the new parameters for the naïve Bayes classifier. Our new approach has shown to be quite competitive related to simple naïve Bayes. Experimental tests have been done with 21 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
Domingos, P., Pazzani, M.: Beyond independence: conditions for the optimality of the simple Bayesian classifier. In: Proceedings of the 13th International Conference on Machine Learning, pp. 105–112 (1996)
Dougherty, J., Kohavi, R., Sahami, M.: Supervised and unsupervised discretization of continuous features. In: Proceedings of the 12th International Conference on Machine Learning, pp. 194–202 (1995)
Duda, R., Hart, P.: Pattern Classification and Scene Analysis. John Wiley and Sons, Chichester (1973)
Fayyad, U., Irani, K.: Multi-interval discretization of continuous-valued attributes for classification learning. In: Proceedings of the 13th International Conference on Artificial Intelligence, pp. 1022–1027 (1993)
Ferreira, J.T.A.S., Denison, D.G.T., Hand, D.J.: Weighted naïve Bayes modelling for data mining. Technical report, Deparment of Mathematics, Imperial College (May 2001)
Friedman, N., Geiger, D., Goldszmidt, D.M.: Bayesian network classifiers. Machine Learning 29(2-3), 131–163 (1997)
Gama, J.: Iterative Bayes. Intelligent Data Analysis 4, 475–488 (2000)
Good, I.J.: The Estimation of Probabilities: An Essay on Modern Bayesian Methods. MIT Press, Cambridge (1965)
Hand, D.J., Yu, K.: Idiot’s Bayes - not so stupid after all? International Statistical Review 69(3), 385–398 (2001)
Kohavi, J., Becker, B., Sommerfield, D.: Improving simple Bayes. Technical report, Data Mining and Visualization Group, Silicon Graphics (1997)
Kohavi, R.: Scaling up the accuracy of naïve-Bayes classifiers: a decision-tree hybrid. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 202–207 (1996)
Kohavi, R., John, G., Long, R., Manley, D., Pfleger, K.: MLC++: A machine learning library in C++. In: Tools with Artificial Intelligence, pp. 740–743. IEEE Computer Society Press, Los Alamitos (1994)
Kononenko, I.: Semi-naïve Bayesian classifier. In: Sixth European Working Session on Learning, pp. 206–219 (1991)
Langley, P.: Induction of recursive Bayesian classifiers. In: European Conference on Machine Learning, pp. 153–164. Springer, Berlin (1993)
Langley, P., Sage, S.: Induction of selective Bayesian classifiers. In: Morgan Kaufmann (ed.) Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, Seattle, WA, pp. 399–406 (1994)
Larrañaga, P., Lozano, J.A.: Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation. Kluwer Academic Publisher, Dordrecht (2001)
Mühlenbein, H.: The equation for response to selection and its use for prediction. Evolutionary Computation 5, 303–346 (1998)
Murphy, P.M., Aha, D.W.: UCI repository of machine learning databases (1995), http://www.ics.uci.edu/~mlearn/
Pazzani, M.: Searching for dependencies in Bayesian classifiers. In: Proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, pp. 239–248 (1996)
Ramoni, M., Sebastiani, P.: Robust learning with missing data. Machine Learning 45(2), 147–170 (2001)
Ting, K.M.: Discretization of continuous-valued attributes and instance-based learning. Technical Report 491, University of Sydney (1994)
Webb, G.I., Pazzani, M.J.: Adjusted probability naïve Bayesian induction. In: Australian Joint Conference on Artificial Intelligence, pp. 285–295 (1998)
Zaffalon, M., Wesnes, K., Petrini, O.: Credal classification for dementia screening. In: Quaglini, S., Barahona, P., Andreassen, S. (eds.) AIME 2001. LNCS (LNAI), vol. 2101, pp. 67–76. Springer, Heidelberg (2001)
Zhou, W., Greiner, R.: Learning accurate belief nets. Technical report, University of Alberta (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Robles, V., Larrañaga, P., Peña, J.M., Menasalvas, E., Pérez, M.S. (2003). Interval Estimation Naïve Bayes. In: R. Berthold, M., Lenz, HJ., Bradley, E., Kruse, R., Borgelt, C. (eds) Advances in Intelligent Data Analysis V. IDA 2003. Lecture Notes in Computer Science, vol 2810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45231-7_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-45231-7_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40813-0
Online ISBN: 978-3-540-45231-7
eBook Packages: Springer Book Archive