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A Framework for Optimization of Genetic Programming Evolved Classifier Expressions Using Particle Swarm Optimization

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Book cover Hybrid Artificial Intelligence Systems (HAIS 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6076))

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Abstract

Genetic Programming has emerged as an efficient algorithm for classification. It offers several prominent features like transparency, flexibility and efficient data modeling ability. However, GP requires long training times and suffers from increase in average population size during evolution. The aim of this paper is to introduce a framework to increase the accuracy of classifiers by performing a PSO based optimization approach. The proposed hybrid framework has been found efficient in increasing the accuracy of classifiers (expressed in the form of binary expression trees) in comparatively lesser number of function evaluations. The technique has been tested using five datasets from the UCI ML repository and found efficient.

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References

  1. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  2. Flockhart, I.W., Radcliffe, N.J.: GA-MINER: Parallel Data Mining with Hierarchical Genetic Algorithms. University of Edinburgh, Edinburgh (1995)

    Google Scholar 

  3. Smart, W., Zhang, M.: Multiclass Object Classification using Genetic Programming. LNCS, pp. 367–376. Springer, Heidelberg (2004)

    Google Scholar 

  4. Kishore, J.K., et al.: Application of Genetic Programming for Multicategory Pattern Classification. IEEE Transactions on Eolutionary Computation (2000)

    Google Scholar 

  5. Bojarczuk, C.C., Lopes, H.S., Freitas, A.A.: Genetic Programming for Knowledge Discovery in Chest-Pain Giagnosis. IEEE Engineering in Medicine and Biology Magazine, 38–44 (2000)

    Google Scholar 

  6. Koza, J.R.: Concept formation and decision tree induction using the genetic programming paradigm. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 124–128. Springer, Heidelberg (1991)

    Chapter  Google Scholar 

  7. Li, Q., et al.: Dynamic Split-Point Selection Method for Decision Tree Evolved by Gene Expression Programming. In: IEEE Congress on Evolutionary Computation. IEEE Press, Los Alamitos (2009)

    Google Scholar 

  8. Rivero, D., Rabunal, J.R., Pazos, A.: Modifying Genetic Programming for Artificial Neural Network Development for Data Mining. Soft Computing 13, 291–305 (2008)

    Article  Google Scholar 

  9. Ritchie, M.D., et al.: Genetic programming Neural Networks: A powerful bioinformatics tool for human genetics. Applied Soft Computing, 471–479 (2007)

    Google Scholar 

  10. Tsakonas, A.: A comparison of classification accuracy of four genetic programming-evolved intelligent structures. Information Sciences, 691–724 (2006)

    Google Scholar 

  11. Oltean, M., Diosan, L.: An Autonomous GP-based System for Regression and Classification Problems. Applied Soft Computing 9, 49–60 (2009)

    Article  Google Scholar 

  12. Pappa, G.A., Freitas, A.A.: Evolving Rule Induction Algorithms with Multiobjective Grammer based Genetic Programming. Knowledge and Information Systems (2008)

    Google Scholar 

  13. Eggermont, J.: Evolving Fuzzy Decision Trees for Data Classification. In: Proceedings of the 14th Belgium Netherlands Artificial Intelligence Conference (2002)

    Google Scholar 

  14. Konig, R., Johansson, U., Niklasson, L.: Genetic Programming - A Tool for Flexible Rule Extraction. In: IEEE Congress on Evolutionary Computation (2007)

    Google Scholar 

  15. Engelbrecht, A.P., Schoeman, L., Rouwhorst, S.: A Building Block Approach to Genetic Programming for Rule Discovery. In: Abbass, H.A., Sarkar, R., Newton, C. (eds.) Data Mining, pp. 175–189. Idea Group Publishing (2001)

    Google Scholar 

  16. Carreno, E., Leguizamon, G., Wagner, N.: Evolution of Classification Rules for Comprehensible Knowledge Discovery. In: IEEE Congress on Evolutionary Computation, pp. 1261–1268 (2007)

    Google Scholar 

  17. Freitas, A.A.: A Genetic Programming Framework For Two Data Mining Tasks: Classification And Generalized Rule Induction. In: Genetic Programming, pp. 96–101. Morgan Kaufmann, CA (1997)

    Google Scholar 

  18. Kuo, C.S., Hong, T.P., Chen, C.L.: Applying genetic programming technique in classification trees. Soft Computing 11, 1165–1172 (2007)

    Article  MATH  Google Scholar 

  19. Eggermont, J., Eiben, A.E., Hemert, J.I.: A comparison of genetic programming variants for data classification. In: Proceedings of the Eleventh Belgium Netherlands Conference on Artificial Intelligence, pp. 253–254 (1999)

    Google Scholar 

  20. Eggermont, J., Kok, J.N., Kosters, W.A.: GP For Data Classification, Partitioning The Search Space. In: Proceedings of the 2004 Symposium on Applied Computing, pp. 1001–1005 (2004)

    Google Scholar 

  21. Tunstel, E., Jamshidi, M.: On Genetic Programming of Fuzzy Rule-Based Systems for Intelligent Control. International Journal of Intelligent Automation and Soft Computing, 273–284 (1996)

    Google Scholar 

  22. Berlanga, F.J., et al.: A Genetic-Programming-Based Approach for the Learning of Compact Fuzzy Rule-Based Classification Systems. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M., et al. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 182–191. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  23. Mendes, R.R.F., et al.: Discovering Fuzzy Classification Rules with Genetic Programming and Co-Evolution. In: Siebes, A., De Raedt, L., et al. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 314–325. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  24. Zhang, M., Ciesielski, V.: Genetic Programming For Multiple Class object Detection. In: Proceedings of the 12th Australian Joint Conference on Artificial Intelligence, Australia, pp. 180–192 (1999)

    Google Scholar 

  25. Parrott, D., Li, X., Ciesielski, V.: Multi-objective techniques in genetic programming for evolving classifiers. In: IEEE Congress on Evolutionary Computation, pp. 183–190 (2005)

    Google Scholar 

  26. Smart, W.R., Zhang, M.: Classification Strategies for Image Classification in Genetic Programming. In: Proceeding of Image and Vision Computing NZ International Conference, pp. 402–407 (2003)

    Google Scholar 

  27. Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)

    Google Scholar 

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Authors and Affiliations

  1. National University of Computer and Emerging Sciences, H-11/4, Islamabad, Pakistan

    Hajira Jabeen & Abdul Rauf Baig

Authors
  1. Hajira Jabeen
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  2. Abdul Rauf Baig
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Editor information

Editors and Affiliations

  1. Facultad de informatica UPV/EHU, San Sebastian, Spain

    Manuel Graña Romay  & M. Teresa Garcia Sebastian  & 

  2. Universidad de Salamanca, Spain

    Emilio Corchado

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Jabeen, H., Baig, A.R. (2010). A Framework for Optimization of Genetic Programming Evolved Classifier Expressions Using Particle Swarm Optimization. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_7

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  • DOI: https://doi.org/10.1007/978-3-642-13769-3_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13768-6

  • Online ISBN: 978-3-642-13769-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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