Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Design of a binary decision tree using genetic algorithm for recognition of the defect patterns of cold mill strip

유전 알고리듬을 이용한 이진 트리 분류기의 설계와 냉연 흠 분류에의 적용

  • 김경민 (여수대학교 전기공학과) ;
  • 이병진 (동양공업전문대학 전기전자통신공학부 전자분야) ;
  • 류경 (고려대학교 전기공학과) ;
  • 박귀태 (고려대학교 전기전자전파공학부)
  • Published : 2000.01.01
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Abstract

This paper suggests a method to recognize the various defect patterns of a cold mill strip using a binary decision tree automatically constructed by a genetic algorithm(GA). In classifying complex patterns with high similarity like the defect patterns of a cold mill stirp, the selection of an optimal feature set and an appropriate recognizer is important to achieve high recognition rate. In this paper a GA is used to select a subset of the suitable features at each node in the binary decision tree. The feature subset with maximum fitness is chosen and the patterns are classified into two classes using a linear decision function. This process is repeated at each node until all the patterns are classified into individual classes. In this way, the classifier using the binary decision tree is constructed automatically. After constructing the binary decision tree, the final recognizer is accomplished by having neural network learning sits of standard patterns at each node. In this paper, the classifier using the binary decision tree is applied to the recognition of defect patterns of a cold mill strip, and the experimental results are given to demonstrate the usefulness of the proposed scheme.

Keywords

References

  1. M. D. Levine, 'Feature extraction : a survey', Proc. of the IEEE, pp. 1391-1407, 1969
  2. A. K. Jain and R. Dubes, 'Feature definition in pattern recognition with small sample size', Pattern Recog., vol. 10, pp. 85-97, 1978 https://doi.org/10.1016/0031-3203(78)90016-X
  3. L. Yao, 'Nonparametric learning of decision regions via the genetic algorithm,' IEEE Trans. Sys. Man, Cybern., vol. 26, pp. 313-321, Apr. 1996 https://doi.org/10.1109/3477.485882
  4. Practical Handbook of decision regions via the genetic Algorithms L. Chambers
  5. L. Chambers, Practical Handbook of decision regions via the genetic Algorithms, CRC Press, 1995
  6. L. Yao and W. A. Sethares, 'Nonlinear parameter estimation via the genetic algorithm,' IEEE Trans. Signal Proc. vol. 42, pp. 927-935, Apr. 1994 https://doi.org/10.1109/78.285655
  7. J. J. Grefenstette, 'Optimization of control parameter for genetic algorithms,' IEEE Trans. Sys.,Man, Cybern, vol. 16, pp. 122-128, Jan/Feb. 1986 https://doi.org/10.1109/TSMC.1986.289288
  8. J. K. Mui and K. S. Fu, 'Automated classification of nucleated blood cells using a binary tree classifier,' IEEE Trans. Patt. Anal. Mach. Intl., vol. 5, pp. 429-443, Sep. 1980
  9. S. R. Safavian and D. Landrebe, 'A survey of decision tree classifier methodology,' IEEE Trans. Sys., Man, Cybern, vol. 21, pp. 660-674 https://doi.org/10.1109/21.97458
  10. H. J. Payne and W. S. Meisel, 'An algorithm for constructing optimal binary decision trees,' IEEE Trans. Comters, vol. 26, pp. 905-916, Sep. 1977 https://doi.org/10.1109/TC.1977.1674938
  11. G. H. Landeweerd, T. Timmers, and E. S. Gelsema, 'Binary tree versus single level tree classification of white blood cells,' Pattern Recognition, vol. 16, pp. 571-577 https://doi.org/10.1016/0031-3203(83)90073-0
  12. P. H. Swain and H. Hauska, 'The decision tree classification :design and potential,' IEEE Trans. Geosci. Elec., vol. 15, pp. 142-147, Jul. 1977
  13. C. Y. Suen and W. R. Wang, 'ISOETRP : An interactive clustering algorithm with new object,' Pattern Recognition, vol. 7, pp. 211-219, 1984 https://doi.org/10.1016/0031-3203(84)90060-8
  14. G. P. Babu and M. N. Murty, 'Clustering with evolution strategies,' Pattern Recognition, vol. 27, pp. 321-329, 1994 https://doi.org/10.1016/0031-3203(94)90063-9
  15. S.Z. Selim and K.Alsultan, 'A simulated annealing algorithm for the clustering problem,' Pattern Recognition, vol. 24, pp. 1003-1008, 1991 https://doi.org/10.1016/0031-3203(91)90097-O
  16. 정순원, 박귀태, '유전 알고리즘을 이용한 이진 결정 트리의 설계와 응용,' 전자공학회 논문지, 제33권, B편, 6호, pp. 1122-1130, 1996
  17. B. J. Lee, K. Lyou, G. T. Park, K. M. Kim, 'Design of a binary decision tree for recognition of the defect patterns of cold mill strip using genetic algorithm,' Proc. of the Third Asian Fuzzy Systems Symposium, pp. 208-212, Korea, June 1988
  18. 김경민, 박귀태, 박중조, 외, '냉연 표면흠 검사 알고리듬의 개발,' 제어.자동화.시스템공학회, 제3권, 제2호, pp. 179-186, 1997, 4