Abstract
Multiple classifier systems (MCS) have become popular during the last decade. Self-generating neural tree (SGNT) is a suitable base-classifier for MCS because of the simple setting and fast learning capability. However, the computation cost of the MCS increases in proportion to the number of SGNTs. In an earlier paper, we proposed a pruning method for the structure of the SGNT in the MCS to reduce the computational cost. In this paper, we propose a novel pruning method for more effective processing and we call this model self-organizing neural grove (SONG). The pruning method is constructed from both an on-line and an off-line pruning method. Experiments have been conducted to compare the SONG with an unprunedMCS based on SGNT, anMCS based on C4.5, and the k-nearest neighbor method. The results show that the SONG can improve its classification accuracy as well as reducing the computation cost.
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
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2000)
Breiman, L.: Bagging predictors. Machine Learning 24, 123–140 (1996)
Schapire, R.E.: The strength of weak learnability. Machine Learning 5(2), 197–227 (1990)
Quinlan, J.R.: Bagging, Boosting, and C4.5. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence, Portland, OR, Augest 4-8, 1996, pp. 725–730. AAAI Press, The MIT Press (1996)
Rätsch, G., Onoda, T., Müller, K.R.: Soft margins for AdaBoost. Machine Learning 42(3), 287–320 (2001)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, New York (1995)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons Inc., New York (2000)
Wen, W.X., Jennings, A., Liu, H.: Learning a neural tree. In: the International Joint Conference on Neural Networks, Beijing, China, November 3-6, 1992, vol. 2, pp. 751–756 (1992)
Kohonen, T.: Self-Organizing Maps. Springer, Berlin (1995)
Inoue, H., Narihisa, H.: Improving generalization ability of self-generating neural networks through ensemble averaging. In: Terano, T., Liu, H., Chen, A.L.P. (eds.) PAKDD 2000. LNCS, vol. 1805, pp. 177–180. Springer, Heidelberg (2000)
Inoue, H., Narihisa, H.: Optimizing a multiple classifier system. In: Ishizuka, M., Sattar, A. (eds.) PRICAI 2002. LNCS (LNAI), vol. 2417, pp. 285–294. Springer, Heidelberg (2002)
Stone, M.: Cross-validation: A review. Math. Operationsforsch. Statist. Ser. Statistics 9(1), 127–139 (1978)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Blake, C., Merz, C.: UCI repository of machine learning databases (1998)
Patrick, E.A., Frederick, P., Fischer, I.: A generalized k-nearest neighbor rule. Information and Control 16(2), 128–152 (1970)
Zhang, B., Srihari, S.N.: Fast k-nearest neighbor classification using cluster-based trees. IEEE Transactions on Pattern and Machine Intelligence 26(4), 525–528 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Inoue, H. (2009). Self-Organizing Neural Grove: Efficient Multiple Classifier System with Pruned Self-Generating Neural Trees. In: Franco, L., Elizondo, D.A., Jerez, J.M. (eds) Constructive Neural Networks. Studies in Computational Intelligence, vol 258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04512-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-04512-7_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04511-0
Online ISBN: 978-3-642-04512-7
eBook Packages: EngineeringEngineering (R0)