Abstract
This paper presents a method to recognize the various defect patterns of a cold mill strip using a binary decision tree constructed by genetic algorithm(GA). In this paper, GA was 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. 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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© 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 Genetic Algorithm for Recognizing Defect Patterns of Cold Mill Strip. In: Tawfik, A.Y., Goodwin, S.D. (eds) Advances in Artificial Intelligence. Canadian AI 2004. Lecture Notes in Computer Science(), vol 3060. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24840-8_38
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DOI: https://doi.org/10.1007/978-3-540-24840-8_38
Publisher Name: Springer, Berlin, Heidelberg
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