Abstract
Multi-layer neural networks have been successfully applied in a wide range of supervised and unsupervised learning applications. However defining its architecture is a difficult task, and might make their usage very complicated. To solve this problem, a rule-based model, KBANN, was previously introduced making use of an apriori knowledge to build the network architecture. Neithertheless this apriori knowledge is not always available when dealing with real world applications. Other methods presented in the literature propose to find directly the neural network architecture by incrementally adding new hidden neurons (or layers) to the existing network, network which initially has no hidden layer. Recently, a novel neural network approach CLANN based on concept lattices was proposed with the advantage to be suitable for finding the architecture of the neural network when the apriori knowledge is not available. However CLANN is limited to application with only two-class data, which is not often the case in practice. In this paper we propose a novel approach M-CLANN in order to treat multi-class data. Carried out experiments showed the soundness and efficiency of our approach on different UCI datasets compared to standard machine learning systems. It also comes out that M-CLANN model considerably improved CLANN model when dealing with two-class data.
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References
Andrews, R., Diederich, J., Tickle, A.B.: Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems 8(6), 373–389 (1995)
Bastide, Y., Pasquier, N., Taouil, R., Stumme, G., Lakhal, L.: Mining minimal non-redundant association rules using frequent closed itemsets. In: CL 2000. LNCS (LNAI), vol. 1861, pp. 972–986. Springer, Heidelberg (2000)
Cibas, T., Fogelman, F., Gallinari, P., Raudys, S.: Variable Selection with Optimal Cell Damage. In: International conference on Artificial Neural Network (ICANN 1994), vol. 1, pp. 727–730 (1994)
Curran, D., O’Riordan, C.: Applying Evolutionary Computation to designing Neural Networks: A study of the State of the art. Technical report NUIG-IT-111002, department of Information Technology, NUI of Galway (2002)
Duch, W., Setiono, R., Zurada, J.M.: Computational intelligence methods for understanding of data. Proceedings of the IEEE 92(5), 771–805 (2004)
Kuznetsov, S., Obiedkov, S.: Comparing Performance of Algorithms for Generating Concept Lattices. JETAI 14(2/3), 189–216 (2002)
Ganter, B., Wille, R.: Formal Concepts Analysis: Mathematical foundations. Springer, Heidelberg (1999)
Gasmi, G., Ben Yahia, S., Mephu Nguifo, E., Slimani, Y.: A new informative generic base of association rules. In: Advances in Knowledge Discovery and Data Mining (PAKDD), vol. 3518, pp. 81–90 (2005)
Han, J., Hamber, M.: Data Mining: Concepts and Techniques. Morgan Kauffman Publishers, San Francisco (2001)
Le Cun, Y., Denker, J.S., Solla, S.A.: Optimal Brain Damage. In: Advances in Neural Information Processing Systems 2, pp. 598–605. Morgan Kaufmann Publishers, San Francisco (1990)
Newmann, D.J., Hettich, S., Blake, C.L., Merz, C.J.: (UCI)Repository of machine learning databases. Dept. Inform. Comput. Sci., Univ. California, Irvine, CA (1998), http://www.ics.uci.edu/AI/ML/MLDBRepository.html
Fu, H., Fu, H., Njiwoua, P., Mephu Nguifo, E.: Comparative study of FCA-based supervised classification algorithms. In: The proc of 2nd ICFCA, Sydney, pp. 313–320 (2004)
Parekh, R., Yang, J., Honavar, V.: Constructive Neural-Network Learning Algorithms for Pattern Classification. IEEE Transactions on Neural Networks 11, 436–451 (2000)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by backpropagating errors. Nature 323, 318–362 (1986)
Shavlik, J.W., Towell, G.G.: Kbann: Knowledge based articial neural networks. Artificial Intelligence 70, 119–165 (1994)
Stumme, G., Taouil, R., Bastide, Y., Pasquier, N., Lakhal, L.: Computing Iceberg concept lattices with TITANIC. Journal on Knowledge and Data Engineering (KDE) 2(42), 189–222 (2002)
Tsopze, N., Mephu Nguifo, E., Tindo, G.: CLANN: Concept-Lattices-based Artificial Neural Networks. In: Diatta, J., Eklund, P., Liquiére, M. (eds.) Proceedings of fifth Intl. Conf. on Concept Lattices and Applications (CLA 2007), Montpellier, France, October 24-26, 2007, pp. 157–168 (2007)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco (2005)
Yacoub, M., Bennani, Y.: Architecture Optimisation in Feedforward Connectionist Models. In: 8th International Conference on Artificial Neural Networks (ICANN 1998), Skövde, Sweden (1998)
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Mephu Nguifo, E., Tsopzé, N., Tindo, G. (2008). M-CLANN: Multi-class Concept Lattice-Based Artificial Neural Network for Supervised Classification. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87559-8_84
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DOI: https://doi.org/10.1007/978-3-540-87559-8_84
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