Abstract
Rule learning is known for its descriptive and therefore comprehensible classification models which also yield good class predictions. However, in some application areas, we also need good class probability estimates. For different classification models, such as decision trees, a variety of techniques for obtaining good probability estimates have been proposed and evaluated. However, so far, there has been no systematic empirical study of how these techniques can be adapted to probabilistic rules and how these methods affect the probability-based rankings. In this paper we apply several basic methods for the estimation of class membership probabilities to classification rules. We also study the effect of a shrinkage technique for merging the probability estimates of rules with those of their generalizations.
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
Asuncion, A., Newman, D.J.: UCI machine learning repository (2007)
Cestnik, B.: Estimating probabilities: A crucial task in Machine Learning. In: Aiello, L. (ed.) Proceedings of the 9th European Conference on Artificial Intelligence (ECAI 1990), Stockholm, Sweden, pp. 147–150. Pitman (1990)
Chen, S.F., Goodman, J.T.: An empirical study of smoothing techniques for language modeling. Technical Report TR-10-98, Computer Science Group, Harvard University, Cambridge, MA (1998)
Cohen, W.W.: Fast effective rule induction. In: Prieditis, A., Russell, S. (eds.) Proceedings of the 12th International Conference on Machine Learning (ML 1995), Lake Tahoe, CA, pp. 115–123. Morgan Kaufmann, San Francisco (1995)
Demsar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)
Ferri, C., Flach, P.A., Hernández-Orallo, J.: Improving the AUC of probabilistic estimation trees. In: Proceedings of the 14th European Conference on Machine Learning, Cavtat-Dubrovnik, Croatia, pp. 121–132 (2003)
Fürnkranz, J.: Pruning algorithms for rule learning. Machine Learning 27(2), 139–171 (1997)
Fürnkranz, J., Flach, P.A.: Roc ’n’ rule learning-towards a better understanding of covering algorithms. Machine Learning 58(1), 39–77 (2005)
Fürnkranz, J., Widmer, G.: Incremental Reduced Error Pruning. In: Cohen, W., Hirsh, H. (eds.) Proceedings of the 11th International Conference on Machine Learning (ML 1994), New Brunswick, NJ, pp. 70–77. Morgan Kaufmann, San Francisco (1994)
Hand, D.J., Till, R.J.: A simple generalisation of the area under the roc curve for multiple class classification problems. Machine Learning 45(2), 171–186 (2001)
Hüllermeier, E., Vanderlooy, S.: Why fuzzy decision trees are good rankers. IEEE Transactions on Fuzzy Systems (to appear, 2009)
Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. The MIT Press, Cambridge (1999)
Provost, F.J., Domingos, P.: Tree induction for probability-based ranking. Machine Learning 52(3), 199–215 (2003)
Wang, B., Zhang, H.: Improving the ranking performance of decision trees. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 461–472. Springer, Heidelberg (2006)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sulzmann, JN., Fürnkranz, J. (2009). An Empirical Comparison of Probability Estimation Techniques for Probabilistic Rules. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds) Discovery Science. DS 2009. Lecture Notes in Computer Science(), vol 5808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04747-3_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-04747-3_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04746-6
Online ISBN: 978-3-642-04747-3
eBook Packages: Computer ScienceComputer Science (R0)