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M-CLANN: Multiclass Concept Lattice-Based Artificial Neural Network

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Constructive Neural Networks

Part of the book series: Studies in Computational Intelligence ((SCI,volume 258))

Abstract

Multilayer feedforward neural networks have been successfully applied in different domains. Defining an interpretable architecture of a multilayer perceptron (MLP) for a given problem is still challenging. We propose a novel approach based on concept lattices to automatically design a neural network architecture. The designed architecture can then be trained with the backpropagation algorithm. We report experimental results obtained on different datasets, and then discuss our contribution as a means to provide semantics to each neuron in order to build an interpretable neural network.

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References

  1. Andrews, R., Diederich, J., Tickle, A.: Surevy and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems 8(6), 373–389 (1995)

    Article  Google Scholar 

  2. Bastide, Y., Pasquier, N., Taouil, R., Stumme, G., Lakhal, L.: Mining minimal non-redundant association rules using frequent closed itemsets. In: Palamidessi, C., Moniz Pereira, L., Lloyd, J.W., Dahl, V., Furbach, U., Kerber, M., Lau, K.-K., Sagiv, Y., Stuckey, P.J. (eds.) CL 2000. LNCS (LNAI), vol. 1861, pp. 972–986. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  3. Bertini Jr., J.R., do Carmo Nicoletti, M.: MBabCoNN - A Multiclass Version of a Constructive Neural Network Algorithm Based on Linear Separability and Convex Hull. In: Kůrková, V., Neruda, R., Koutník, J. (eds.) ICANN 2008, Part II. LNCS, vol. 5164, pp. 723–733. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  4. Carpineto, C., Romano, G.: Concept Data Analysis: Theory and Applications. John Wiley and Sons, Chichester (2004)

    Book  MATH  Google Scholar 

  5. Cibas, T., Fogelman, F., Gallinari, P., Raudys, S.: Variable Selection with Optimal Cell Damage. In: International conference on Artificial Neural Network (ICANN 1994), Part I, pp. 727–730 (1994)

    Google Scholar 

  6. Cornuéjols, A., Miclet, L.: Apprentissage Artificiel: Concepts et algorithmes, Eyrolles (2002)

    Google Scholar 

  7. Mitchell., T.M.: Machine Learning. McGraw-Hill, New York (1997)

    Google Scholar 

  8. Curran, D., O’Riordan, C.: Applying Evolutionary Computation to designing Neural Networks: A study of the State of the art department of Information Technology, Technical report, NUI Galway (2002)

    Google Scholar 

  9. Darbari, A.: Rule extraction from trained ANN: A Survey, Technical report, Institute of Artificial Intelligence, Dept. of Computer Science, TU Dresden, Germany (2001)

    Google Scholar 

  10. Dreyfus, G., Samuelides, M., Martinez, J.M., Gordon, M., Badran, F., Thiria, S., Hérault, L.: Réseaux de Neurones: Méthodologie et applications. Eyrolles (2002)

    Google Scholar 

  11. Duch, W., Setiono, R., Zurada, J.M.: Computational intelligence methods for understanding of data. Proceedings of the IEEE 92(5), 771–805 (2004)

    Article  Google Scholar 

  12. Endres, D., Fldiák, P.: An application of formal concept analysis to Neural Decoding. In: Belohlavek, E.R., Kuznetsov, S.O. (eds.) proceedings of sixth Intl. Conf. on Concept Lattices and Applications (CLA), pp. 181–192 (2008)

    Google Scholar 

  13. Frean, M.: The Upstart algorithm: A method for constructing and training feed forward neural networks. Neural computation (4), 198–209 (1992)

    Article  Google Scholar 

  14. Fu, H., Fu, H., Njiwoua, P., Nguifo, E.M.: A comparative study of FCA-based supervised classification algorithms. In: Eklund, P. (ed.) ICFCA 2004. LNCS (LNAI), vol. 2961, pp. 313–320. Springer, Heidelberg (2004)

    Google Scholar 

  15. Gallant, S.: Perceptron based learning algorithms. IEEE Transactions Neural Networks 1, 179–191 (1990)

    Article  Google Scholar 

  16. Ganter, B., Wille, R.: Formal Concepts Analysis: Mathematical foundations. Springer, Heidelberg (1999)

    Google Scholar 

  17. Garcez d’Avila, A.S., Broda, K., Gabbay, D.M.: Symbolic knowledge extraction from trained neural networks: a sound approach. Artificial Intelligence 125, 155–207 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  18. Gasmi, G., Ben Yahia, S., Mephu Nguifo, E., Slimani, Y.: A new informative generic base of association rules. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 81–90. Springer, Heidelberg (2005)

    Google Scholar 

  19. Guigues, J.L., Duquenne, V.: Familles minimales d’implications informatives resultant d’un tableau de donnes binaires. Mathmatiques et sciences sociales 95, 5–18 (1986)

    MathSciNet  Google Scholar 

  20. Han, J., Kamber, M.: Datamining: Concepts and Techniques. Morgan Kauffman Publishers, San Francisco (2001)

    Google Scholar 

  21. Hertz, J., Krogh, A., Palmer, R.G.: Introduction to the theory of neural computation. Lecture Notes, Santa Fe Institute. Addison Wesley Publishing, Reading (1991)

    Google Scholar 

  22. Kuznetsov, S., Obiedkov, S.: Comparing Performance of Algorithms for Generating Concept Lattices. JETAI 14(2/3), 189–216 (2002)

    Article  MATH  Google Scholar 

  23. Le Cun, Y., Denker, J.S., Solla, S.A.: Optimal Brain Damage. In: Advances in Neural Information Processing Systems, vol. 2, pp. 598–605. Morgan Kaufmann Publishers, San Francisco (1990)

    Google Scholar 

  24. Mephu Nguifo, E.: Une nouvelle approche base sur le treillis de Galois pour l’apprentissage de concepts. Mathématiques, Informatique, Sciences Humaines 134, 19–38 (1994)

    Google Scholar 

  25. Mephu Nguifo, E., Tsopzé, N., Tindo, G.: M-CLANN: Multi-class concept lattice-based artificial neural network for supervised classification. In: Kůrková, V., Neruda, R., Koutník, J. (eds.) ICANN 2008,, Part II. LNCS, vol. 5164, pp. 812–821. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  26. 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

  27. Parekh, R., Yang, J., Honavar, V.: Constructive Neural Networks Learning Algorithms for Multi-Category Classification. Department of Computer Science Lowa State University Tech. Report ISU CS TR 95-15 (1995)

    Google Scholar 

  28. Parekh, R., Yang, J., Honavar, V.: Constructive Neural-Network Learning Algorithms for Pattern Classification. IEEE Transactions on neural networks 11(2), 436–451 (2000)

    Article  Google Scholar 

  29. Piccinini, G.: Some neural networks compute, others dont. Neural Network 21 (special issue), 311–321 (2008)

    Google Scholar 

  30. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by backpropagating errors. Nature (323), 318–362 (1986)

    Article  Google Scholar 

  31. Rudolph, S.: Using FCA for Encoding Closure Operators into Neural Networks. In: Priss, U., Polovina, S., Hill, R. (eds.) ICCS 2007. LNCS (LNAI), vol. 4604, pp. 321–332. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  32. Shavlik, W.J., Towell, G.G.: Kbann: Knowledge based articial neural networks. Artificial Intelligence (70), 119–165 (1994)

    Article  MATH  Google Scholar 

  33. 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)

    Article  Google Scholar 

  34. Subirats, J.L., Franco, L., Molina Conde, I., Jerez, J.M.: Active learning using a constructive neural network algorithm. In: Kůrková, V., Neruda, R., Koutník, J. (eds.) ICANN 2008,, Part II. LNCS, vol. 5164, pp. 803–811. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  35. Tsopze, N., Mephu Nguifo, E., Tindo, G.: CLANN: Concept-Lattices-based Artificial Neural Networks. In: Diatta, J., Eklund, P., Liquire, M. (eds.) Proceedings of fifth Intl. Conf. on Concept Lattices and Applications (CLA 2007), Montpellier, France, October 24-26, 2007, pp. 157–168 (2007)

    Google Scholar 

  36. Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco (2005)

    MATH  Google Scholar 

  37. Yacoub, M., Bennani, Y.: Architecture Optimisation in Feedforward Connectionist Models. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327. Springer, Heidelberg (1997)

    Google Scholar 

  38. Yang, J., Parekh, R., Honavar, V.: Distal: An Inter-pattern Distance-based Constructive Learning Algorithm: Intell. Data Anal. 3, 55–73 (1999)

    Article  MATH  Google Scholar 

  39. Werbos, P.J.: Why neural networks? In: Fiesler, E., Beale, R. (eds.) Handbook of Neural Computation, pp. A2.1:1–A2.3:6. IOP Pub., Oxford University Press, Oxford (1997)

    Google Scholar 

  40. Wille, R.: Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts. In: Rival, I. (ed.) Ordered Sets, pp. 445–470 (1982)

    Google Scholar 

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Author information

Authors and Affiliations

  1. CRIL CNRS, Artois University, Lens, France

    Engelbert Mephu Nguifo & Norbert Tsopze

  2. Computer Science Department, University of Yaoundé I, PO Box 812, Yaoundé - Cameroon

    Norbert Tsopze & Gilbert Tindo

  3. LIMOS CNRS, Université Blaise Pascal, Clermont Ferrand, France

    Engelbert Mephu Nguifo

Authors
  1. Engelbert Mephu Nguifo
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  2. Norbert Tsopze
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  3. Gilbert Tindo
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science, University of Malaga , Campus de Teatinos S/N, 29071, Málaga, Spain

    Leonardo Franco  & José M. Jerez  & 

  2. Centre for Computational Intelligence, School of Computing, De Montfort University, The Gateway , LE1 9BH, Leicester, UK

    David A. Elizondo

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Nguifo, E.M., Tsopze, N., Tindo, G. (2009). M-CLANN: Multiclass Concept Lattice-Based Artificial Neural Network. 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_6

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  • DOI: https://doi.org/10.1007/978-3-642-04512-7_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04511-0

  • Online ISBN: 978-3-642-04512-7

  • eBook Packages: EngineeringEngineering (R0)

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