Abstract
One of the most relevant tasks concerning Machine Learning is the induction of classifiers, which can be used to classify or to predict. Those classifiers can be used in an isolated way, or can be combined to build a multiple classifier system. Building many-layered systems or knowing relation between different base classifiers are of special interest. Thus, in this paper we will use the HECIC system which consists of two layers: the first layer is a multiple classifier system that processes all the examples and tries to classify them; the second layer is an individual classifier that learns using the examples that are not unanimously classified by the first layer (incorporating new information). While using this system in a previous work we detected that some combinations that hybridize artificial neural networks (ANN) in one of the two layers seemed to get high-accuracy results. Thus, in this paper we have focused on the study of the improvement achieved by using different kinds of ANN in this two-layered system.
This work has been partially supported by the SESAAME project number TIN2008-06582-C03-03 of the MEC, Spain.
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References
Kononenko, I., Kukar, M.: Machine Learning and Data Mining: Introduction to Principles and Algorithms. Horwood Publishing (2007)
Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)
Hansen, L.K., Salamon, P.: Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 993–1001 (1990)
Gama, J., Brazdil, P.: Cascade generalization. Machine Learning 41, 315–343 (2000)
Utgoff, P.E., Stracuzzi, D.J.: Many-layered learning. Neural Computation 14(10), 2497–2529 (2002)
Chindaro, S., Sirlantzis, K., Fairhurst, M.C.: Modelling multiple-classifier relationships using bayesian belief networks. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 312–321. Springer, Heidelberg (2007)
Ramos-Jiménez, G., del Campo-Ávila, J., Morales-Bueno, R.: Hybridizing ensemble classifiers with individual classifiers. In: International Conference on Intelligent Systems Design and Applications. Workshop on Hybrid Learning for Artificial Neural Networks: Architectures and Applications, pp. 199–202. IEEE Computer Society, Los Alamitos (2009)
Ramos-Jiménez, G., del Campo-Ávila, J., Morales-Bueno, R.: ML-CIDIM: Multiple layers of multiple classifier systems based on CIDIM. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W.P., Hu, X. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 138–146. Springer, Heidelberg (2005)
Freund, Y.: Boosting a weak learning algorithm by majority. Information and Computation 121, 256–285 (1995)
Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)
Aslam, J.A., Decatur, S.E.: General bounds on statistical query learning and pac learning with noise via hypothesis boosting. Information and Computation 141, 85–118 (1998)
Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Machine Learning 36, 105–142 (1999)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proceedings of the 13th International Conference on Machine Learning (ICML-1996), pp. 146–148 (1996)
Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)
Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufmann, San Francisco (1993)
John, G.H., Langley, P.: Estimating continuous distributions in bayesian classifiers. In: Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence (UAI-1995), pp. 338–345. Morgan Kaufmann, San Francisco (1995)
Gill, P.E., Murray, W., Wright, M.H.: Practical optimization. Academic Press, London (1981)
Kohonen, T.: Self-organizing maps. Springer-Verlag, New York, Inc. (1997)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: An update. In: SIGKDD Explorations, vol. 11, pp. 10–18 (2009)
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del Campo-Ávila, J., Ramos-Jiménez, G., Pérez-García, J., Morales-Bueno, R. (2011). Studying the Hybridization of Artificial Neural Networks in HECIC. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21498-1_18
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