Abstract
This paper proposes a method to recognize the various defect patterns of a cold mill strip using a binary decision. In classifying complex patterns with high similarity like these defect patterns, the selection of an optimal feature set and an appropriate recognizer is a pre-requisite to a high recognition rate. In this paper GA and K-means algorithm were 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 divided 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 can be constructed automatically, and the final recognizer is implemented by a neural network trained by standard patterns at each node. Experimental results are given to demonstrate the usefulness of the proposed scheme.
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References
Kim, K.M.: Design of a Binary Decision Tree for Recognition of the Defect Patterns of Cold Mill Strip Using Generic Algorithm. In: Proc. AFSS, pp. 208–212 (1998)
Brill, F., Brown, D., Martin, W.: Fast Genetic Selection of Features for Neural Network Classifiers. IEEE Trans. Neural Networks 32, 324–328 (1992)
Yao, L.: Nonparametric Learning of Decision Regions via the Genetic Algorithm. IEEE Trans. Sys. Man Cybern. 26, 313–321 (1996)
Mui, J.K., Fu, K.S.: Automated Classification of Nucleated Blood Cells using a Binary Tree Classifier. IEEE Trans. Pattern Analysis Machine Intelligence 5, 429–443 (1980)
Safavian, S.R., Landgrebe, D.: A Survey of Decision Tree Classifier Methodology. IEEE Trans. Sys. Man Cybern. 21, 660–674 (1991)
Payne, H.J., Meisel, W.S.: An Algorithm for Constructing Optimal Binary Decision Trees. IEEE Trans. Computers 26, 905–916 (1997)
Swain, P.H., Hauska, H.: The Decision Tree Classifier: Design and Potential. IEEE Trans. Geosci. Elec. 15, 142–147 (1997)
Jung, S.W., Park, G.T.: Design and Application of Binary Decision Tree using Genetic Algorithm. Journal of the KITE 33(6), 1122–1130 (1996)
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© 2004 Springer-Verlag Berlin Heidelberg
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Kim, K.M., Park, J.J., Song, M.H., Kim, I.C., Suen, C.Y. (2004). Binary Decision Tree Using K-means and Genetic Algorithm for Recognizing Defect Patterns of Cold Mill Strip. In: Orchard, B., Yang, C., Ali, M. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2004. Lecture Notes in Computer Science(), vol 3029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24677-0_36
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DOI: https://doi.org/10.1007/978-3-540-24677-0_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22007-7
Online ISBN: 978-3-540-24677-0
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