Abstract
Creating an effective ensemble of clauses for large, skewed data sets requires finding a diverse, high-scoring set of clauses and then combining them in such a way as to maximize predictive performance. We have adapted the RankBoost algorithm in order to maximize area under the recall-precision curve, a much better metric when working with highly skewed data sets than ROC curves. We have also explored a range of possibilities for the weak hypotheses used by our modified RankBoost algorithm beyond using individual clauses. We provide results on four large, skewed data sets showing that our modified RankBoost algorithm outperforms the original on area under the recall-precision curves.
Appears In the ILP-2009 Springer LNCS Post-conference Proceedings.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Cortes, C., Mohri, M.: AUC optimization vs. error rate minimization. In: Neural Information Processing Systems (NIPS), MIT Press, Cambridge (2003)
Davis, J., Burnside, E., Dutra, I., Page, D., Costa, V.: An integrated approach to learning Bayesian networks of rules. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 84–95. Springer, Heidelberg (2005)
Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
Dietterich, T.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)
Dutra, I., Page, D., Costa, V., Shavlik, J.: An empirical evaluation of bagging in inductive logic programming. In: Matwin, S., Sammut, C. (eds.) ILP 2002. LNCS (LNAI), vol. 2583, pp. 48–65. Springer, Heidelberg (2002)
Džeroski, S., Lavrac, N.: An introduction to inductive logic programming. In: Proceedings of Relational Data Mining, pp. 48–66 (2001)
Freund, Y., Iyer, R., Schapire, R., Singer, Y.: An efficient boosting algorithm for combining preferences. In: Proceedings of 15th International Conference on Machine Learning, pp. 170–178 (1998)
Freund, Y., Schapire, R.: Experiments with a new boosting algorithm. In: Proceedings of the 13th International Conference on Machine Learning, pp. 148–156 (1996)
Goadrich, M., Oliphant, L., Shavlik, J.: Gleaner: Creating ensembles of first-order clauses to improve recall-precision curves. Machine Learning 64(1-3), 231–261 (2006)
Quinlan, J.R.: Relational learning and boosting. In: Relational Data Mining, pp. 292–306 (2001)
Ray, S., Craven, M.: Representing sentence structure in hidden Markov models for information extraction. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence (2001)
Srinivasan, A.: The Aleph manual version 4 (2003), http://web.comlab.ox.ac.uk/oucl/research/areas/machlearn/Aleph/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Oliphant, L., Burnside, E., Shavlik, J. (2010). Boosting First-Order Clauses for Large, Skewed Data Sets. In: De Raedt, L. (eds) Inductive Logic Programming. ILP 2009. Lecture Notes in Computer Science(), vol 5989. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13840-9_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-13840-9_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13839-3
Online ISBN: 978-3-642-13840-9
eBook Packages: Computer ScienceComputer Science (R0)