A generic optimising feature extraction method using multiobjective genetic programming

Applied Soft Computing Journal

Volumn 11, Issue 1, 2011, Pages 1087-1097

  Zhang, Yang ^a   Rockett, Peter I ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

Feature extraction; Genetic programming; Multiobjective optimisation; Pattern recognition

Indexed keywords

CLASS SEPARABILITY; CONVENTIONAL CLASSIFIER; DECISION SPACE; FEATURE EXTRACTION METHODS; FEATURE EXTRACTOR; INPUT PATTERNS; MACHINE-LEARNING; MULTIOBJECTIVE GENETIC PROGRAMMING; MULTIOBJECTIVE OPTIMISATION; REAL-WORLD DATASETS; STATLOG;

CLASSIFIERS; GENETIC PROGRAMMING; MULTIOBJECTIVE OPTIMIZATION; PATTERN RECOGNITION SYSTEMS;

FEATURE EXTRACTION;

EID: 77957916556     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2010.02.008     Document Type: Article

References (45)

1. D. Addison, S. Wermter, and G. Arevian A comparison of feature extraction and selection techniques International Conference on Artificial Neural Networks - Supplementary Proceedings Istanbul, Turkey 2003 212 215
2. C.H. Park, H. Park, P. Pardalos, A comparative study of linear and nonlinear feature extraction methods, Technical Report TR 04-042, Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, 2004.
3. I. Guyon, A. Elisseeff, An introduction to feature extraction, in: I. Guyon, S. Gunn, M. Nikravesh, L. Zadeh (Eds.), Feature Extraction, Foundations and Applications, Physica-Verlag, Springer, 2006.
4. R. Poli, W.B. Langdon, N.F. McPhee, A Field Guide to Genetic Programming, Published via http://lulu.com and freely available at http://www.gp-field- guide.org.uk, 2008.
5. M. Ebner On the evolution of interest operators using genetic programming Late Breaking Papers in 1st European Workshop on Genetic Programming Paris, France 1998 6 10
6. M. Ebner, and A. Zell Evolving a task specific image operator Joint Proceedings of the 1st European Workshop on Evolutionary Image Analysis, Signal Processing and Telecommunications (EvoIASP'99 and EuroEcTel'99) Göteborg, Sweden 1999 74 89
7. M.J.C. Bot Feature extraction for the k-nearest neighbour classifier with genetic programming EuroGP 2001 Lake Como, Italy 2001 256 267
8. J.R. Koza Genetic Programming II: Automatic Discovery of Reusable Programs 1994 MIT Press Cambridge, MA
9. W.A. Tackett Genetic programming for feature discovery and image discrimination 5th International Conference on Genetic Algorithms 1993 303 309
10. N.R. Harvey, J. Theiler, S.P. Brumby, S. Perkins, J.J. Szymanski, J.J. Bloch, R.B. Porter, G. Mark, A. Young, C, comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction, IEEE Transactions on Geoscience and Remote Sensing 40 (2002) 393-404.
11. J.R. Sherrah, R.E. Bogner, and A. Bouzerdoum The evolutionary preprocessor: automatic feature extraction for supervised classification using genetic programming 2nd Annual Conference on Genetic Programming Palo Alto, CA 1997 304 312
12. C. Harris, An investigation into the application of genetic programming techniques to signal analysis and feature detection, PhD Thesis, Department of Computer Science, University College, London, 1997.
13. M. Kotani, M. Nakai, and K. Azakawa Feature extraction using evolutionary computation Congress on Evolutionary Computation 1999 1230 1236
14. K. Krawiec Genetic programming-based construction of features for machine learning and knowledge discovery tasks Genetic Programming and Evolvable Machines 3 2002 329 343
15. C. Estébanez, R. Aler, and J.M. Valls A method based on genetic programming for improving the quality of datasets in classification problems International Journal of Computer Science and Applications 4 2007 69 80
16. H. Guo, L.B. Jack, and A.K. Nandi Feature generation using genetic programming with application to fault classification IEEE Transactions on Systems, Man and Cybernetics, Part B 35 2005 89 99 (Pubitemid 40226201)
17. M.G. Smith, and L. Bull Genetic programming with a genetic algorithm for feature construction and selection Genetic Programming and Evolvable Machines 6 2005 265 281
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 2 2001 61 73
19. C.A.C. Coello An updated survey of GA-based multiobjective optimization techniques ACM Computing Surveys 32 2000 109 143
20. S. Bleuler, M. Brack, L. Theile, and E. Zitzler Multiobjective genetic programming: reducing bloat using SPEA2 in Congress on Evolutionary Computation 2001 536 543
21. Y. Jin, and B. Sendhoff Pareto-based multiobjective machine learning: an overview and case studies IEEE Transactions on Systems, Man and Cybernetics, Part C 38 2008 397 415
22. C.L. Blake, C.J. Merz, UCI Repository of Machine Learning Databases [http://www.ics.uci.edu/ mlearn/MLRepository.html], University of California, Dept of Information Computer Science, Irvine, CA, 1998.
23. D. Michie, D.J. Spiegelhalter, and C.C. Taylor Machine Learning, Neural and Statistical Classification 1994 Ellis Horwood Upper Saddle River, NJ
24. T. Lim, W. Loh, and Y. Shih A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms Machine Learning 40 2000 203 228
25. K. Fukunaga Introduction to Statistical Pattern Recognition 2nd ed. 1990 Academic Press San Diego, CA
26. C. 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 28 1998 26 37 (Pubitemid 128748052)
27. K. Deb, A. Pratap, S. Agarawal, and T. Meyarivan A fast and elitist multiobjective genetic algorithm: NSGA-II IEEE Transactions on Evolutionary Computing 6 2002 182 197
28. A. Jaszkiewicz Genetic local search for multi-objective combinatorial optimization European Journal of Operational Research 137 2002 50 71
29. J.D. Knowles, L. Thiele, E. Zitzler, A tutorial on the performance assessment of stochastic multiobjective optimizers, Technical Report TIK-214, Computer Engineering and Networks Laboratory, ETH, Zurich, Switzerland, 2005.
30. E. Zitzler, L. Thiele, An evolutionary algorithm for multiobjective optimization: the strength Pareto approach, Technical Report 43, Computer Engineering and Communications Networks Lab (TIK), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland, 1998.
31. Y. Zhang, and P.I. Rockett A comparison of three evolutionary strategies for multiobjective genetic programming Artificial Intelligence Reviews 27 2007 149 163
32. R. Kumar, and P.I. Rockett Improved sampling of the Pareto-front in multiobjective genetic optimizations by steady-state evolution: a Pareto converging genetic algorithm Evolutionary Computation 10 2002 283 314
33. T. Ito, H. Iba, and S. Sato Non-destructive depth-dependent crossover for genetic programming 1st European Workshop on Genetic Programming Paris, France 1998 14 15
34. J.R. Koza, F.H. Bennett, D. Andre, and M.A. Keane Genetic Programming III: Darwinian Invention and Problem Solving 1999 Morgan Kaufmann San Francisco, CA
35. O.L. Mangasarian, W.N. Street, and W.H. Wolberg Breast cancer diagnosis and prognosis via linear programming Operations Research 43 1995 570 577
36. O.L. Mangasarian, and W.H. Wolberg Cancer diagnosis via linear programming SIAM News 23 1990 1 18
37. J.R. Quinlan C4.5: Programs for Machine Learning 1993 Morgan Kaufmann San Mateo, CA
38. I.H. Witten, and E. Frank Data Mining: Practical Machine Learning Tools 2nd ed. 2005 Morgan Kaufmann San Francisco, CA
39. R.O. Duda, P.E. Hart, and P.E. Stork Pattern Recognition 2nd ed. 2001 John Wiley and Son New York
40. T. Dietterich Approximate statistical tests for comparing supervised classification learning algorithms Neural Computation 10 1998 1895-1923
41. E. Alpaydin Combined 5 × 2 cv F test for comparing supervised classification learning algorithms Neural Computation 11 1999 1885 1892
42. D.P. Muni, N.R. Pal, and J. Das A novel approach to design classifiers using genetic programming IEEE Transactions on Evolutionary Computation 8 2004 183 196
43. M.J.C. Bot, and W.B. Langdon Application of genetic programming to induction of linear classification trees 11th Belgium/Netherlands Conference on Artificial Intelligence 1999 107 114
44. T. Loveard, and V. Ciesielski Representing classification problems in genetic programming Congress on Evolutionary Computation Seoul, Korea 2001 1070 1077 (Pubitemid 32806258)
45. P. Nordin, and W. Banzhaf Genetic programming controlling a miniature robot Working Notes for the AAAI Symposium on Genetic Programming MIT, Cambridge, MA 1995 61 67