Artificial Immune Recognition System with Nonlinear Resource Allocation Method and Application to Traditional Malay Music Genre Classification

  • Shahram Golzari
  • Shyamala Doraisamy
  • Md Nasir B. Sulaiman
  • Nur Izura Udzir
  • Noris Mohd. Norowi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5132)


Artificial Immune Recognition System (AIRS) has shown an effective performance on several machine learning problems. In this study, the resource allocation method of AIRS was changed with a nonlinear method. This new algorithm, AIRS with nonlinear resource allocation method, was used as a classifier in Traditional Malay Music (TMM) genre classification. Music genre classification has a great important role in music information retrieval systems nowadays. The proposed system consists of three stages: feature extraction, feature selection and finally using proposed algorithm as a classifier. Based on results of conducted experiments, the obtained classification accuracy of proposed system is 88.6 % using 10 fold cross validation for TMM genre classification. The results also show that AIRS with nonlinear allocation method obtains maximum classification accuracy for TMM genre classification.


Artificial Immune System AIRS Music Genre Classification Nonlinear Resource allocation 


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  1. 1.
    Dannenberg, R., Foote, J., Tzanetakis, G., Weare, C.: Panel: New Directions in Music Information Retrieval. In: International Computer Music Conference, International Computer Music Association, pp. 52–59 (2001)Google Scholar
  2. 2.
    Tzanetakis, G., Cook, P.: Musical Genre Classification of Audio Signals. IEEE Transactions on Speech and Audio Processing 10(5) (2002)Google Scholar
  3. 3.
    Wold, E., Blum, T., Keislar, D., Wheaton, J.: Content-based Classification, Search, and Retrieval of Audio. IEEE Multimedia 3(3), 27–36 (1996)CrossRefGoogle Scholar
  4. 4.
    Norowi, N.M., Doraisiamy, S., Rahmat, R.W.: Traditional malaysian musical genres classification based on the analysis of beat feature in audio. Journal of Information Technology in Asia, JITA 2 (2007)Google Scholar
  5. 5.
    de Castro, L.N., Timmis, J.: Artificial Immune Systems as a novel Soft Computing Paradigm. Soft Computing Journal 7(7) (2003)Google Scholar
  6. 6.
    de Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, Heidelberg (2002)zbMATHGoogle Scholar
  7. 7.
    Watkins, A.: AIRS: A Resource Limited Artificial Immune Classifier. M.S. thesis, Department of Computer Science. Mississippi State University (2001)Google Scholar
  8. 8.
    Matusky, P.: Malaysian Shadow Play and Music: Continuity of an Oral Tradition. Oxford University Press, Kuala Lumpur (1993)Google Scholar
  9. 9.
    Ang, M.: A Layered Architectural Model for Music: Malaysian Music on the World Wide Web. Ph.D. dissertation, UPM (1998)Google Scholar
  10. 10.
    Becker, J.: The Percussive Patterns in the Music of Mainland Southeast Asia. Ethnomusicology 2(2), 173–191 (1968)CrossRefGoogle Scholar
  11. 11.
    Hall, M.A., Smith, L.A.: Practical feature subset selection for machine learning. In: Proceedings of the 21st Australian Computer Science Conference, pp. 181–191 (1998)Google Scholar
  12. 12.
    Witten, H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  13. 13.
    Carter, J.H.: The immune systems as a model for pattern recognition and classification. Journal of the American Medical Informatics Association 7(1), 28–41 (2000)Google Scholar
  14. 14.
    Timmis, J., Neal, M.: A Resource Limited Artificial Immune System. Knowledge Based Systems 14(3), 121–130 (2001)CrossRefGoogle Scholar
  15. 15.
    Marwah, G., Boggess, L.: Artificial immune systems for classification: Some issues. In: Proceedings of the first international conference on artificial immune systems, University of Kent Canterbury, England, pp. 149–153 (2002)Google Scholar
  16. 16.
    Watkins, A., Boggess, L.: A new classifier based on resource limited artificial immune systems. In: Congress on Evolutionary Computation. Part of the World Congress on Computational Intelligence, Honolulu, HI, pp. 1546–1551 (2002)Google Scholar
  17. 17.
    Watkins, A., Timmis, J.: Artificial Immune Recognition System (AIRS): Revisions and Refinements. In: 1st International Conference on Artificial Immune Systems (ICARIS 2002), Canterbury, UK, pp. 173–181 (2002)Google Scholar
  18. 18.
    Watkins, A.: Exploiting Immunological Metaphors in the Development of Serial, Parallel, and Distributed Learning Algorithms. PhD Thesis, Computer Science, University of Kent, Canterbury, England (2005)Google Scholar
  19. 19.
    Watkins, A., Timmis, J., Boggess, L.: Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm. Genetic Programming and Evolvable Machines 5(3), 291–317 (2004)CrossRefGoogle Scholar
  20. 20.
    Frank, E., Witten, I.H.: Generating Accurate Rule Sets without Global Optimization. In: Fifteenth International Conference on Machine Learning. Morgan Kaufmann, San Francisco (1998)Google Scholar
  21. 21.
    Keerthi, S.S., et al.: Improvements to Platt’s SMO Algorithm for SVM Classifier Design. Neural Computation 13(3), 637–649 (2001)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Shahram Golzari
    • 1
    • 2
  • Shyamala Doraisamy
    • 1
  • Md Nasir B. Sulaiman
    • 1
  • Nur Izura Udzir
    • 1
  • Noris Mohd. Norowi
    • 1
  1. 1.Faculty of Computer Science and Information TechnologyUniversiti Putra MalaysiaSerdangMalaysia
  2. 2.Electrical and Computer Engineering DepartmentHormozgan UniversityBandarabbasIran

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