Advertisement

A New Model for Classifying DNA Code Inspired by Neural Networks and FSA

  • Byeong Kang
  • Andrei Kelarev
  • Arthur Sale
  • Ray Williams
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4303)

Abstract

This paper introduces a new model of classifiers CL(V,E,ℓ,r) designed for classifying DNA sequences and combining the flexibility of neural networks and the generality of finite state automata. Our careful and thorough verification demonstrates that the classifiers CL(V,E,ℓ,r) are general enough and will be capable of solving all classification tasks for any given DNA dataset. We develop a minimisation algorithm for these classifiers and include several open questions which could benefit from contributions of various researchers throughout the world.

Keywords

Neural Network Equivalence Class State Automaton Finite State Automaton Error Correct Output Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baldi, P., Brunak, S.: Bioinformatics: The Machine Learning Approach. MIT Press, Cambridge (2001)MATHGoogle Scholar
  2. 2.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms. The MIT Press, Cambridge (2001)MATHGoogle Scholar
  3. 3.
    Dazeley, R.P., Kang, B.H.: Weighted MCRDR: deriving information about relationships between classifications. In: MCRDR, AI 2003: Advances in Artificial Intelligence, Perth, Australia, pp. 245–255 (2003)Google Scholar
  4. 4.
    Dazeley, R.P., Kang, B.H.: An online classification and prediction hybrid system for knowledge discovery in databases. In: Proc. AISAT 2004, The 2nd Internat. Conf. Artificial Intelligence in Science and Technology, Hobart, Tasmania, pp. 114–119 (2004)Google Scholar
  5. 5.
    Durbin, R., Eddy, S.R., Krogh, A., Mitchison, G.: Biological Sequence Analysis. Cambridge University Press, Cambridge (1999)Google Scholar
  6. 6.
    Eilenberg, S.: Automata, Languages, and Machines, vol. A,B. Academic Press, New York (1974)MATHGoogle Scholar
  7. 7.
    Gallian, J.A.: Graph labeling. Electronic J. Combinatorics, Dynamic Survey DS6, 148 (January 20, 2005), http://www.combinatorics.org
  8. 8.
    Gusfield, D.: Algorithms on Strings, Trees, and Sequences. In: Computer Science and Computational Biology, Cambridge University Press, Cambridge (1997)Google Scholar
  9. 9.
    Holub, J., Iliopoulos, C.S., Melichar, B., Mouchard, L.: Distributed string matching using finite automata, Combinatorial Algorithms. In: AWOCA 1999, Perth, pp. 114–127 (1999)Google Scholar
  10. 10.
    Jones, N.C., Pevzner, P.A.: An Introduction to Bioinformatics Algorithms. MIT Press, Cambridge (2004), http://www.bioalgorithms.info/ Google Scholar
  11. 11.
    Kang, B.H.: Pacific Knowledge Acquisition Workshop, Auckland, New Zealand (2004)Google Scholar
  12. 12.
    Kelarev, A.V.: Ring Constructions and Applications. World Scientific, Singapore (2002)MATHGoogle Scholar
  13. 13.
    Kelarev, A.V.: Graph Algebras and Automata. Marcel Dekker, New York (2003)MATHGoogle Scholar
  14. 14.
    Kelarev, A.V., Miller, M., Sokratova, O.V.: Directed graphs and closure properties for languages. In: Baskoro, E.T. (ed.) Proc.12 Australasian Workshop on Combinatorial Algorithms, Putri Gunung Hotel, Lembang, Bandung, Indonesia, July 14–17, pp. 118–125 (2001)Google Scholar
  15. 15.
    Kelarev, A.V., Miller, M., Sokratova, O.V.: Languages recognized by two-sided automata of graphs. Proc. Estonian Akademy of Science 54(1), 46–54 (2005)MATHMathSciNetGoogle Scholar
  16. 16.
    Kelarev, A.V., Sokratova, O.V.: Languages recognized by a class of finite automata. Acta Cybernetica 15, 45–52 (2001)MATHMathSciNetGoogle Scholar
  17. 17.
    Kelarev, A.V., Sokratova, O.V.: Directed graphs and syntactic algebras of tree languages. J. Automata, Languages & Combinatorics 6(3), 305–311 (2001)MATHMathSciNetGoogle Scholar
  18. 18.
    Kelarev, A.V., Sokratova, O.V.: Two algorithms for languages recognized by graph algebras. Internat. J. Computer Math. 79(12), 1317–1327 (2002)MATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Kelarev, A.V., Sokratova, O.V.: On congruences of automata defined by directed graphs. Theoret. Computer Science 301, 31–43 (2003)MATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Kelarev, A.V., Trotter, P.G.: A combinatorial property of automata, languages and their syntactic monoids. In: Proceedings of the Internat. Conf. Words, Languages and Combinatorics III, Kyoto, Japan, pp. 228–239 (2003)Google Scholar
  21. 21.
    Lee, K.H., Kay, J., Kang, B.H.: Keyword association network: a statistical multi-term indexing approach for document categorization. In: Proc. Fifth Australasian Document Computing Symposium, Brisbane, Australia, pp. 9–16 (2000)Google Scholar
  22. 22.
    Lee, K., Kay, J., Kang, B.H.: KAN and RinSCut: lazy linear classifier and rank-in-score threshold in similarity-based text categorization. In: Proc. ICML-2002 Workshop on Text Learning, University of New South Wales, Sydney, Australia, pp. 36–43 (2002)Google Scholar
  23. 23.
    Lee, K.H., Kay, J., Kang, B.H., Rosebrock, U.: A Comparative Study on Statistical Machine Learning Algorithms and Thresholding Strategies for Automatic Text Categorization. In: Ishizuka, M., Sattar, A. (eds.) PRICAI 2002. LNCS (LNAI), vol. 2417, pp. 444–453. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  24. 24.
    Lee, K.H., Kang, B.H.: A new framework for uncertainty sampling: exploiting uncertain and positive-certain examples in similarity-based text classification. In: Proc. Internat. Conf. on Information Technology: Coding and Computing (ITCC 2004), Las Vegas, Nevada, p. 12 (2004)Google Scholar
  25. 25.
    Luger, G.F.: Artificial Intelligence. Structures and Strategies for Complex Problem Solving. Addison-Wesley, Reading (2005)Google Scholar
  26. 26.
    Mount, D.: Bioinformatics: Sequence and Genome Analysis, Cold Spring Harbor Laboratory (2001), http://www.bioinformaticsonline.org/
  27. 27.
    Park, S.S., Kim, Y., Park, G., Kang, B.H., Compton, P.: Automated information mediator for HTML and XML Based Web information delivery service. In: Proc. 18th Australian Joint Conf. on Artificial Intelligence, Sydney, pp. 401–404 (2005)Google Scholar
  28. 28.
    Park, G.S., Kim, Y.S., Kang, B.H.: Synamic mobile content adaptation according to various delivery contexts. J. Security Engineering 2, 202–208 (2005)Google Scholar
  29. 29.
    Park, G.S., Kim, Y.T., Kim, Y., Kang, B.H.: SOAP message processing performance enhancement by simplifying system architecture. J. Security Engineering 2, 163–170 (2005)Google Scholar
  30. 30.
    Park, G.S., Park, S., Kim, Y., Kang, B.H.: Intelligent web document classification using incrementally changing training data Set. J. Security Engineering 2, 186–191 (2005)Google Scholar
  31. 31.
    Păun, G., Salomaa, A.: New Trends in Formal Languages. Springer, Berlin (1997)Google Scholar
  32. 32.
    Petrovskiy, M.: Probability Estimation in Error Correcting Output Coding Framework Using Game Theory. In: Zhang, S., Jarvis, R. (eds.) AI 2005. LNCS (LNAI), vol. 3809, pp. 186–196. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  33. 33.
    Pin, J.E. (ed.): LITP 1988. LNCS, vol. 386. Springer, Heidelberg (1989)MATHGoogle Scholar
  34. 34.
    Rozenberg, G., Salomaa, A.: Handbook of Formal Languages. In: Word, Language, Grammar, vol. 1, Springer, Berlin (1997)Google Scholar
  35. 35.
    Smyth, B.: Computing Patterns in Strings. Addison-Wesley, Reading (2003)Google Scholar
  36. 36.
    Tuga, M., Miller, M.: Δ-Optimum Exclusive Sum Labeling of Certain Graphs with Radius One. In: Akiyama, J., Baskoro, E.T., Kano, M. (eds.) IJCCGGT 2003. LNCS, vol. 3330, pp. 216–225. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  37. 37.
    Sugeng, K.A., Miller, M., Slamin, Bača, M.: ( a, d)-Edge-Antimagic Total Labelings of Caterpillars. In: Akiyama, J., Baskoro, E.T., Kano, M. (eds.) IJCCGGT 2003. LNCS, vol. 3330, pp. 169–180. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  38. 38.
    van Leeuwen, J.: Handbook of Theoretical Computer Science. In: Algorithms and Complexity, vol. A,B, Elsevier, Amsterdam (1990)Google Scholar
  39. 39.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Byeong Kang
    • 1
  • Andrei Kelarev
    • 1
  • Arthur Sale
    • 1
  • Ray Williams
    • 1
  1. 1.School of ComputingUniversity of TasmaniaHobartAustralia

Personalised recommendations