skip to main content
10.1145/1276958.1277291acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
Article

Improving the human readability of features constructed by genetic programming

Authors Info & Claims
Published:07 July 2007Publication History

ABSTRACT

The use of machine learning techniques to automatically analyse data for information is becoming increasingly widespread. In this paper we examine the use of Genetic Programming and a Genetic Algorithm to pre-process data before it is classified by an external classifier. Genetic Programming is combined with a Genetic Algorithm to construct and select new features from those available in the data, a potentially significant process for data mining since it gives consideration to hidden relationships between features. We then examine techniques to improve the human readability of these new features and extract more information about the domain.

References

  1. Aha, D., & Kibler, D. Instance-based learning algorithms. Machine Learning vol.6, 1991, 37--66. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Ahluwalia, M. & Bull, L. Co-Evolving Functions in Genetic Programming: Classification using k-nearest neighbour. In GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, 1999 pp. 947--952.Google ScholarGoogle Scholar
  3. Bernstein, Y., Li, X., Ciesielski, V., Song, A.: Multiobjective parsimony enforcement for superior generalisation performance. In: Proceedings of the Congress for Evolutionary Computation 2004 (CEC'04), 2004 pp. 83--89.Google ScholarGoogle ScholarCross RefCross Ref
  4. Bojarczuk, C.C., Lopes, H.S., Freitas, A.A., Michalkiewicz, E.L., A constrained-syntax genetic programming system for discovering classification rules: application to medical data sets, Artificial Intelligence in Medicine 30 (1), 2004, 21--48.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. De Jong, E. D., Watson, R. A., Pollack, J. B. Reducing Bloat and Promoting Diversity using Multi-Objective Methods. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), 2001, pp. 11--18.Google ScholarGoogle Scholar
  6. Ekárt, A. & Máárkus, A. Using Genetic Programming and Decision Trees for Generating Structural Descriptions of Four Bar Mechanisms. In Artificial Intelligence for Engineering Design, Analysis and Manufacturing, volume 17, issue 3, 2003.Google ScholarGoogle Scholar
  7. Garcia-Almanza, A.L., Tsang, E.P.K. Simplifying Decision Trees Learned by Genetic Programming. IEEE Congress on Evolutionary Computation, CEC 2006, pp 2142-- 2148.Google ScholarGoogle ScholarCross RefCross Ref
  8. Holland, J.H. Adaptation in Natural and Artificial Systems. Univ. Michigan. 1975. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. John, G.H & Langley, P. Estimating Continuous Distributions in Bayesian Classifiers. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, San Mateo. 1995, 338--345.Google ScholarGoogle Scholar
  10. Kelly, J.D. & Davis, L. Hybridizing the Genetic Algorithm and the K Nearest Neighbors Classification Algorithm. In Proceedings of the Fourth International Conference on Genetic Algorithms. Morgan Kaufmann, 1991, pp377--383.Google ScholarGoogle Scholar
  11. Kohavi, R. & John, G. H. Wrappers for feature subset selection. Artificial Intelligence Journal vol. 1-2: 273--324. 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Koza, J.R. Genetic Programming. MIT Press. 1992.Google ScholarGoogle Scholar
  13. Krawiec, K. Genetic Programming-based Construction of Features for Machine Learning and Knowledge Discovery Tasks. Genetic Programming and Evolvable Machines vol. 3 no. 4: 329--343. 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Langdon, W. B. & Buxton, B. F. Genetic programming for improved receiver operating characteristics. In Second International Conference on Multiple Classifier System, volume 2096: 68--77. 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Mitchell, T. M. Machine Learning. McGraw-Hill, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Otero, F. E. B., Silva, M. M. S., Freitas, A. A. & Nievola J. C. Genetic Programming for Attribute Construction in Data Mining. In Genetic Programming: 6th European Conference, EuroGP 2003, Essex, UK, April 2003, Proceedings. Springer, pp. 384--393.Google ScholarGoogle Scholar
  17. Quinlan, J.R. C4.5: Programs for Machine Learning. Morgan Kaufmann. 1993. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Raymer, M.L., Punch, W., Goodman, E.D. & Kuhn, L. Genetic Programming for Improved Data Mining -- Application to the Biochemistry of Protein Interactions. In Proceedings of the Second Annual Conference on Genetic Programming, Morgan Kaufmann, 1996, 375--380.Google ScholarGoogle Scholar
  19. Siedlecki, W. & Sklansky, J. On Automatic Feature Selection. International Journal of Pattern Recognition and Artificial Intelligence 2:197--220. 1988.Google ScholarGoogle ScholarCross RefCross Ref
  20. Smith, M. & Bull, L. Using Genetic Programming for Feature Creation with a Genetic Algorithm Feature Selector. In Parallel Problem Solving from Nature -- PPSN VIII, X. Springer-Verlag, 2004.Google ScholarGoogle Scholar
  21. Smith, M. & Bull, L. Genetic Programming with a Genetic Algorithm for Feature Construction and Selection. Genetic Programming and Evolvable Machines vol. 6 no. 3: 265--281. 2005 Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Thomas, J. & Sycara, K. The Importance of Simplicity and Validation in Genetic Programming for Data Mining in Financial Data. Proceedings of the joint AAAI-1999 and GECCO-1999 Workshop on Data Mining with Evolutionary Algorithms, July, 1999.Google ScholarGoogle Scholar
  23. Vafaie, H. & De Jong, K. Genetic Algorithms as a Tool for Restructuring Feature Space Representations. In Proceedings of the International Conference on Tools with A.I. IEEE Computer Society Press. 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Witten, I.H. & Frank, E. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann. 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Improving the human readability of features constructed by genetic programming

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
      July 2007
      2313 pages
      ISBN:9781595936974
      DOI:10.1145/1276958

      Copyright © 2007 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 7 July 2007

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • Article

      Acceptance Rates

      GECCO '07 Paper Acceptance Rate266of577submissions,46%Overall Acceptance Rate1,669of4,410submissions,38%

      Upcoming Conference

      GECCO '24
      Genetic and Evolutionary Computation Conference
      July 14 - 18, 2024
      Melbourne , VIC , Australia

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader