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.
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References
J.R. Koza. Genetic Programming II, Automatic Discovery of Reusable Programs. The MIT Press, Cambridge, Massachusetts, 1994
R. O. Duda and P. E. Hart and D. G. Stork. Pattern Classification (2nd Edition), Wiley-Interscience, 2000
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
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
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
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)
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
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
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
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
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
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
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
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
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
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
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
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
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
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
J. Canny. A computational approach to edge detection. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679–698, 1986
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
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
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
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
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
O. L. Mangasarian and W. H. Wolberg. Cancer diagnosis via linear programming. SIAM News, vol. 23, pp. 1–18, 1990
E. Alpaydin. Combined 5 2 cv F-test for comparing supervised classification learning algorithms. Neural Computation, vol. 11, no. 8, pp. 1885–1892, 1999
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
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
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
Y. Zhang and P.I. Rockett. The Bayesian operating point of the Canny edge detector. IEEE Trans. Image Processing, 2005 (submitted)
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
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.
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
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.
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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
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DOI: https://doi.org/10.1007/3-540-33019-4_4
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