Skip to main content
SpringerLink
Log in

Feature Extraction Using Multi-Objective Genetic Programming

  • Chapter
Multi-Objective Machine Learning

Part of the book series: Studies in Computational Intelligence ((SCI,volume 16))

Abstract

A generic, optimal feature extraction method using multi-objective genetic programming (MOGP) is presented. This methodology has been applied to the well-known edge detection problem in image processing and detailed comparisons made with the Canny edge detector. We show that the superior performance from MOGP in terms of minimizing the misclassification is due to its effective optimal feature extraction. Furthermore, to compare different evolutionary approaches, two popular techniques - PCGA and SPGA - have been extended to genetic programming as PCGP and SPGP, and applied to five datasets from the UCI database. Both of these evolutionary approaches provide comparable misclassification errors within the present framework but PCGP produces more compact transformations.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. J.R. Koza. Genetic Programming II, Automatic Discovery of Reusable Programs. The MIT Press, Cambridge, Massachusetts, 1994

    MATH  Google Scholar 

  2. R. O. Duda and P. E. Hart and D. G. Stork. Pattern Classification (2nd Edition), Wiley-Interscience, 2000

    Google Scholar 

  3. D. Addison, S. Wermter and G. Arevian. A comparison of feature extraction and selection techniques. In: Proceedings of the International Conference on Artificial Neural Networks, Istanbul, Turkey, Supplementary Proceedings, pp. 212–215, 2003

    Google Scholar 

  4. M. C. J. Bot. Feature extraction for the k-Nearest neighbor classifier with genetic programming. In: Genetic Programming, Proceedings of EuroGP'2001, Lake Como, Italy, pp. 256–267, 2001

    Google Scholar 

  5. Y. Zhang and P.I. Rockett. Evolving optimal feature extraction using multiobjective genetic programming: A methodology and preliminary study on edge detection. In: GECCO 2005, pp. 795–802, 2005

    Google Scholar 

  6. Y. Zhang and P.I. Rockett. A Generic Optimal Feature Extraction Method using Multiobjective Genetic Programming: Methodology and Applications. IEEE Trans. Systems, Man, and Cybernetics, 2005 (submitted)

    Google Scholar 

  7. S. Bleuler, M. Brack, L. Thiele, and E. Zitzler. Multiobjective genetic programming: Reducing bloat using SPEA2. In: Congress on Evolutionary Computation (CEC 2001), pp. 536–543, 2001

    Google Scholar 

  8. Z.J. Huang, M. Pei, E. Goodman, Y. Huang, and G. Liu. Genetic algorithm optimized feature transformation - A comparison with different classifiers. In: GECCO 2003, LNCS 2724, pp. 2121–2133, 2003

    Google Scholar 

  9. W.A. Tackett. Genetic programming for feature discovery and image discrimination. In: Proceedings of the Fifth International Conference on Genetic Algorithms, Morgan Kaufmann, pp. 303–309, 1993

    Google Scholar 

  10. M. Ebner and A. Zell. Evolving a task specific image operator. In:Joint Proceedings of the First European Workshops on Evolutionary Image Analysis, Signal Processing and Telecommunications (EvoIASP'99 and EuroEcTel'99), Göteborg, Sweden, Springer-Verlag, pp. 74–89, 1999

    Chapter  Google Scholar 

  11. M. Ebner. On the evolution of interest operators using genetic programming. In: Late Breaking Papers at EuroGP'98: the First European Workshop on Genetic Programming, Paris, France, pp. 6–10, 1998

    Google Scholar 

  12. M.D. Heath, S. Sarkar, T. Sanocki, and K.W. Bowyer. Comparison of edge detectors: A methodology and initial study. In: Computer Vision and Pattern Recognition, Proceedings CVPR '96, pp. 143–148, 1996

    Google Scholar 

  13. T. Ito, I. Iba, and S. Sato. Non-destructive depth-dependent crossover for genetic programming. In: Proceedings of the First European Workshop on Genetic Programming, LNCS, Paris, pp. 14–15, 1998

    Google Scholar 

  14. J.R. Sherrah, R.E. Bogner, and A. Bouzerdoum. The evolutionary preprocessor: Automatic feature extraction for supervised classification using genetic programming. In: Genetic Programming 1997: Proceedings of the Second Annual Conference. Stanford University, CA, USA. pp. 304–312, 1997

    Google Scholar 

  15. T. Kanungo and R.M. Haralick. Receiver operating characteristic curves and optimal Bayesian operating points. In: International Conference on Image Processing - Proceedings, vol.3, pp. 256–259, Washington, DC., 1995

    Article  Google Scholar 

  16. C.A.C. Coello. An updated survey of evolutionary multiobjective optimization techniques: State of the art and future trends. In: Congress on Evolutionary Computation, pp. 3–13, Washington, D.C., 1999

    Google Scholar 

  17. E. Zitzler and L. Thiele. An evolutionary algorithm for multiobjective optimization: The strength Pareto approach. Technical Report, 43, Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland, 1998

    Google Scholar 

  18. A. Ekárt and S.Z. Németh. Selection based on the Pareto nondomination criterion for controlling code growth in genetic programming. Genetic Programming and Evolvable Machines, vol. 2, pp. 61–73, 2001

    Article  MATH  Google Scholar 

  19. C.M. Fonseca and P.J. Fleming. Multiobjective optimization and multiple constraint handling with evolutionary algorithms -Part I: A unified formulation. IEEE Transactions on Systems, Man and Cybernetics-Part A: Systems and Humans, vol. 28, pp.26–37, 1998

    Article  Google Scholar 

  20. R. Kumar and P.I. Rockett. Improved sampling of the Pareto-Front in multiobjective genetic optimization by Steady-State evolution: A Pareto converging genetic algorithm. Evolutionary Computation, vol.10, no. 3, pp. 283–314, 2002

    Article  Google Scholar 

  21. J. Canny. A computational approach to edge detection. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679–698, 1986

    Article  Google Scholar 

  22. S. Konishi, A.L. Yuille, J.M. Coughlan, and S.C. Zhu. Statistical edge detection: Learning and evaluating edge cues. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 1, pp. 57–74, 2003

    Article  Google Scholar 

  23. M. Kotani, M. Nakai, and K. Akazawa. Feature extraction using evolutionary computation. In: Proceedings of the Congress of Evolutionary Computation, IEEE Press, pp. 1230–1236, 1999

    Google Scholar 

  24. K. Bowyer, C. Kranenburg, and S. Dougherty. Edge detector evaluation using empirical ROC curves. Computer Vision and Image Understanding, vol.84, no.1, pp. 77–103, 2001

    Article  MATH  Google Scholar 

  25. D. P. Muni, N. R. Pal, and J. Das. A novel approach to design classifiers using genetic programming. IEEE Transactions on Evolutionary Computation, vol. 8, no. 2, pp.183–196, 2004

    Article  Google Scholar 

  26. O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and prognosis via linear programming. Operations Research, vol. 43, no. 4, pp. 570–577, 1995

    Article  MATH  MathSciNet  Google Scholar 

  27. O. L. Mangasarian and W. H. Wolberg. Cancer diagnosis via linear programming. SIAM News, vol. 23, pp. 1–18, 1990

    Google Scholar 

  28. E. Alpaydin. Combined 5 2 cv F-test for comparing supervised classification learning algorithms. Neural Computation, vol. 11, no. 8, pp. 1885–1892, 1999

    Article  Google Scholar 

  29. N.R. Harvey, S.P. Brumby, S. Perkins, J.J. Szymanski, J. Theiler, J.J. Bloch, R.B. Porter, M. Galassi and A.C. Young. Image feature extraction: GENIE vs conventional supervised classification techniques. IEEE Transactions on Geoscience and Remote Sensing, vol. 40, no. 2, pp. 393–404, 2002

    Article  Google Scholar 

  30. W.C. Chen, N.A. Thacker and P.I. Rockett. An adaptive step edge model for self-consistent training of a neural network for probabilistic edge labeling. IEE Proceedings - Vision, Image and Signal Processing, vol. 143, no.1, pp. 41–50, 1996

    Article  Google Scholar 

  31. P.I. Rockett. Performance assessment of feature detection algorithms: A methodology and case study on corner detectors. IEEE Transactions on Image Processing, vol.12, no.11. pp. 1668–1676, 2003

    Article  Google Scholar 

  32. Y. Zhang and P.I. Rockett. The Bayesian operating point of the Canny edge detector. IEEE Trans. Image Processing, 2005 (submitted)

    Google Scholar 

  33. K. Krawiec. Genetic programming-based construction of features for machine learning and knowledge discovery tasks. Genetic Programming and Evolvable Machines, vol.3, no.4, pp.329–343, 2002

    Article  MATH  Google Scholar 

  34. C. Harris. An investigation into the application of genetic programming techniques to signal analysis and feature detection. PhD. thesis, Dept. Comp. Science., Univ. College of London, Sep. 1997.

    Google Scholar 

  35. C.L. Blake and C.J. Merz. UCI Repository of machine learning databases [http://www.ics.uci.edu/ mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science, 1998

    Google Scholar 

  36. W. Schiffmann, M. Joost, and R. Werner. Synthesis and performance analysis of multilayer neural network architectures. Technical Report 16/1992, University of Koblenz, Institute für Physics, 1992.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, S1 3JD, UK

    Yang Zhang & Peter I Rockett

Authors
  1. Yang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter I Rockett
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Honda Research Institute Europe GmbH, Carl-Legien-Str. 30, Offenbach, 63073, Germany

    Yaochu Jin Dr.

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer

About this chapter

Cite this chapter

Zhang, Y., Rockett, P.I. (2006). Feature Extraction Using Multi-Objective Genetic Programming. In: Jin, Y. (eds) Multi-Objective Machine Learning. Studies in Computational Intelligence, vol 16. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-33019-4_4

Download citation

  • DOI: https://doi.org/10.1007/3-540-33019-4_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30676-4

  • Online ISBN: 978-3-540-33019-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation