Abstract
The so-called “boosting” principle was introduced by Schapire and Freund in the 1990s in relation to weak learners in the Probably Approximately Correct computational learning framework. Another practice that has developed in recent years consists in assessing the quality of evolutionary or genetic classifiers with Receiver Operating Characteristics (ROC) curves. Following the RankBoost algorithm by Freund et al., this article is a cross-bridge between these two techniques, and deals about boosting ROC-based genetic programming classifiers. Updating the weights after a boosting round turns to be the algorithm keystone since the ROC curve does not allow to know directly which training cases are learned or misclassified. We propose a geometrical interpretation of the ROC curve to attribute an error measure to every training case. We validate our ROCboost algorithm on several benchmarks from the UCI-Irvine repository, and we compare boosted Genetic Programming performance with published results on ROC-based Evolution Strategies and Support Vector Machines.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Schapire, R.E.: The strength of weak learnability. Machine Learning 5(2), 197–227 (1990)
Freund, Y.: Boosting a weak learning algorithm by majority. Information and Computation 121(2), 256–285 (1995)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Machine Learning: Proceedings of the Thirteenth International Conference, pp. 148–156. Morgan Kaufmann, Seattle (1996)
Breiman, L.: Bagging predictors. Machine Learning 24, 123–140 (1996)
Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. The Annals of Statistics 28(2), 237–407 (2000)
Bradley, A.: The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition 30(7), 1145–1159 (1997)
Provost, F.J., Fawcett, T., Kohavi, R.: The case against accuracy estimation for comparing induction algorithms. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp. 445–453. Morgan Kaufmann, Seattle (1998)
Cohen, W.W., Schapire, R.E., Singer, Y.: Learning to order things. In: NIPS ’97: Proceedings of the 1997 conference on Advances in neural information processing systems 10, pp. 451–457. MIT Press, Cambridge (1998)
Mozer, M.C., et al.: Prodding the roc curve: constrained optimization of classifier performance. In: Advances in Neural Information Processing Systems, vol. 14, MIT Press, Cambridge (2001)
Sebag, M.: ROC-based evolutionary learning: Application to medical data mining. In: Liardet, P., Collet, P., Fonlupt, C., Lutton, E., Schoenauer, M. (eds.) EA 2003. LNCS, vol. 2936, pp. 384–396. Springer, Heidelberg (2004)
Sebag, M., Azé, J., Lucas, N.: Impact studies and sensitivity analysis in medical data mining with ROC-based genetic learning. In: Third IEEE International Conference on Data Mining, ICDM 2003, pp. 637–640. IEEE Computer Society Press, Los Alamitos (2003)
Azé, J., Lucas, N., Sebag, M.: A genetic roc-based classifier. Technical report, LRI, Orsay, France (July 2004)
Freund, Y., Iyer, R., Schapire, R.E., Singer, Y.: An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research 4(6), 933–969 (2003)
Cortes, C., Mohri, M.: Auc optimization vs. error rate minimization. In: Thrun, S., Saul, L., Schölkopf, B. (eds.) Advances in Neural Information Processing Systems 16, MIT Press, Cambridge (2004)
Iba, H.: Bagging, boosting, and bloating in genetic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO’99, pp. 1053–1060. Morgan-Kaufmann, Seattle (1999)
Drucker, H.: Improving regressors using boosting techniques. In: Proceedings of the 14th International Conference on Machine Learning (ICML), pp. 107–115. Morgan Kaufmann, Seattle (1997)
Paris, G., Robilliard, D., Fonlupt, C.: Applying boosting techniques to genetic programming. In: Collet, P., Fonlupt, C., Hao, J.-K., Lutton, E., Schoenauer, M. (eds.) EA 2001. LNCS, vol. 2310, pp. 245–254. Springer, Heidelberg (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Robilliard, D., Marion-Poty, V., Mahler, S., Fonlupt, C. (2007). An Empirical Boosting Scheme for ROC-Based Genetic Programming Classifiers. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds) Genetic Programming. EuroGP 2007. Lecture Notes in Computer Science, vol 4445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71605-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-540-71605-1_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71602-0
Online ISBN: 978-3-540-71605-1
eBook Packages: Computer ScienceComputer Science (R0)