Solving Selected Classification Problems in Bioinformatics Using Multilayer Neural Network Based on Multi-Valued Neurons (MLMVN)

  • Igor Aizenberg
  • Jacek M. Zurada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4668)

Abstract

A multilayer neural network based on multi-valued neurons (MLMVN) is a new powerful tool for solving classification, recognition and prediction problems. This network has a number of specific properties and advantages that follow from the nature of a multi-valued neuron (complex-valued weights and inputs/outputs lying on the unit circle). Its backpropagation learning algorithm is derivative-free. The learning process converges very quickly, and the learning rate for all neurons is self-adaptive. The functionality of the MLMVN is higher than the one of the traditional feedforward neural networks and a variety of kernel-based networks. Its higher flexibility and faster adaptation to the mapping implemented make it possible to solve complex classification problems using a simpler network. In this paper, we show that the MLMVN can be successfully used for solving two selected classification problems in bioinformatics.

References

  1. 1.
    Aizenberg, I., Moraga, C., Paliy, D.: A Feedforward Neural Network based on Multi-Valued Neurons. In: Reusch, B. (ed.) Computational Intelligence, Theory and Applications. Advances in Soft Computing, vol. XIV, pp. 599–612. Springer, Heidelberg (2005)Google Scholar
  2. 2.
    Aizenberg, I., Moraga, C.: Multilayer Feedforward Neural Network Based on Multi-Valued Neurons (MLMVN) and a Backpropagation Learning Algorithm. Soft Computing 11, 169–183 (2007)CrossRefGoogle Scholar
  3. 3.
    Aizenberg, N.N., Ivaskiv Yu., L., Pospelov, D.A: About one generalization of the threshold function. Doklady Akademii Nauk SSSR (The Reports of the Academy of Sciences of the USSR) 196(6), 1287–1290 (1971)Google Scholar
  4. 4.
    Aizenberg, N.N., Aizenberg, I.N.: CNN Based on Multi-Valued Neuron as a Model of Associative Memory for Gray-Scale Images. In: Proceedings of the Second IEEE Int. Workshop on Cellular Neural Networks and their Applications, pp. 36–41. Technical University Munich, Germany (1992)CrossRefGoogle Scholar
  5. 5.
    Aizenberg, N.N., Ivaskiv Yu, L.: Multiple-Valued Threshold Logic (in Russian). Naukova Dumka Publisher House, Kiev (1977)Google Scholar
  6. 6.
    Aizenberg, I., Aizenberg, N., Vandewalle, J.: Multi-valued and universal binary neurons: theory, learning, applications. Kluwer Academic Publishers, Dordrecht (2000)Google Scholar
  7. 7.
    Jankowski, S., Lozowski, A., Zurada, J.M.: Complex-Valued Multistate Neural Associative Memory. IEEE Trans. on Neural Networks 7, 1491–1496 (1996)CrossRefGoogle Scholar
  8. 8.
    Aoki, H., Kosugi, Y.: An Image Storage System Using Complex-Valued Associative Memory. In: Proc. of the 15th International Conference on Pattern Recognition, vol. 2, pp. 626–629. IEEE Computer Society Press, Los Alamitos (2000)Google Scholar
  9. 9.
    Muezzinoglu, M.K., Guzelis, C., Zurada, J.M.: A New Design Method for the Complex-Valued Multistate Hopfield Associative Memory. IEEE Trans. on Neural Networks 14(4), 891–899 (2003)CrossRefGoogle Scholar
  10. 10.
    Aoki, H., Watanabe, E., Nagata, A., Kosugi, Y.: Rotation-Invariant Image Association for Endoscopic Positional Identification Using Complex-Valued Associative Memories. In: Mira, J.M., Prieto, A.G. (eds.) IWANN 2001. LNCS, vol. 2085, pp. 369–374. Springer, Heidelberg (2001)Google Scholar
  11. 11.
    Aizenberg, I., Myasnikova, E., Samsonova, M., Reinitz, J.: Temporal Classification of Drosophila Segmentation Gene Expression Patterns by the Multi-Valued Neural Recognition Method. Journal of Mathematical Biosciences 176(1), 145–159 (2002)MATHCrossRefGoogle Scholar
  12. 12.
    Aizenberg, I., Bregin, T., Butakoff, C., Karnaukhov, V., Merzlyakov, N., Milukova, O.: Type of Blur and Blur Parameters Identification Using Neural Network and Its Application to Image Restoration. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 1231–1236. Springer, Heidelberg (2002)Google Scholar
  13. 13.
    Aizenberg, I., Paliy, D., Astola, J.: Multilayer Neural Network based on Multi-Valued Neurons and the Blur Identification Problem. In: Proceedings of the 2006 IEEE Joint Conference on Neural Networks, Vancouver, Canada, July 16-21, pp. 1200–1207. IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
  14. 14.
    Aizenberg, I., Paliy, D., Moraga, C., Astola, J.: Blur Identification Using Neural Network for Image Restoration. In: Reusch, B. (ed.) Computational Intelligence, Theory and Application. Proceedings of the 9th International Conference on Computational Intelligenc, pp. 441–455. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Monti, S., Tamayo, P., Mesirov, J., Golub, T.: Consensus Clustering: A resampling-based method for class discovery and visualization of gene expression microarray data. Machine Learning 52, 91–118 (2003)MATHCrossRefGoogle Scholar
  16. 16.
    Rumelhart, D.E., McClelland, J.L.: Parallel Distributed Processing: Explorations in the Microstructure of Cognition. MIT Press, Cambridge (1986)Google Scholar
  17. 17.
    Mangasarian, O.L., Setiono, R., Wolberg, W.H.: Pattem recognition via linear programming: Theory and application to medical diagnosis. In: Coleman, T.F., Li, Y. (eds.) Large-scale numerical optimization, pp. 22–30. SIAM Publications, Philadelphia (1990)Google Scholar
  18. 18.
    Wettayapi, W., Lursinsap, C., Chu, C.H.: Rrule extraction from neural networks using fuzzy sets. In: ICONIP’O2. Proceedings of the 9th International Conference on Neural Information Processing, vol. 5, pp. 2852–2856 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Igor Aizenberg
    • 1
  • Jacek M. Zurada
    • 2
  1. 1.Texas A&M University-Texarkana 
  2. 2.University of Louisville 

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