Abstract
We propose a novel ensemble learning algorithm called Triskel, which has two interesting features. First, Triskel learns an ensemble of classifiers that are biased to have high precision (as opposed to, for example, boosting, where the ensemble members are biased to ignore portions of the instance space). Second, Triskel uses weighted voting like most ensemble methods, but the weights are assigned so that certain pairs of biased classifiers outweigh the rest of the ensemble, if their predictions agree. Our experiments on a variety of real-world tasks demonstrate that Triskel often outperforms boosting, in terms of both accuracy and training time.
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
Hansen, L.K., Salamon, P.: Neural Network Ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 993–1001 (1990)
Dietterich, T.G.: Ensemble Methods in Machine Learning. In: First Int. Workshop on Multiple Classifier Systems, New York (2000)
Breiman, L.: Bagging predictors. Machine Learning 24, 123–140 (1996)
Shapire, R.E.: The Strength of Weak Learnability. Machine Learning 5, 197–227 (1990)
Freund, Y.: Boosting a Weak Learning Algorithm by Majority. Information and Computation 121, 256–285 (1995)
Freund, Y., Shapire, R.E.: A Decision-Theoretic Generalization of On-Line Learning. Journal of Computer and System Sciences 55, 119–139 (1997)
Vapnik, V.N.: Vosstanovlenije Zavisimostej po Empiricheskim Dannym. Nauka (1979) (in Russian)
Murphey, Y.L., Guo, H., Feldkamp, L.A.: Neural learning from unbalanced data. Appl. Intell. 21, 117–128 (2004)
Muhlbaier, M., Topalis, A., Polikar, R.: Learn++.MT: A New Approach to Incremental Learning. In: 5th Int. Workshop on Multiple Classifier Systems, Cagliari, Italy (2004)
Akbani, R., Kwek, S., Japkowicz, N.: Applying Support Vector Machines to Imabalanced Datasets. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 39–50. Springer, Heidelberg (2004)
Fürnkranz, J.: Separate and Conquer Rule Learning. Art. Intell. Review 13, 3–54 (1999)
Platt, J.C.: 12. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Fast Training of Support Vector Machines using Sequential Minimal Optimization, pp. 185–208. MIT Press, Cambridge (1999)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (1999)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: Synthetic Minority Over-sampling TEchnique. Journal of Artificial Intelligence Research 16, 341–378 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Heß, A., Khoussainov, R., Kushmerick, N. (2005). Ensemble Learning with Biased Classifiers: The Triskel Algorithm. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2005. Lecture Notes in Computer Science, vol 3541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494683_23
Download citation
DOI: https://doi.org/10.1007/11494683_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26306-7
Online ISBN: 978-3-540-31578-0
eBook Packages: Computer ScienceComputer Science (R0)