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Binary decision tree using K-means and genetic algorithm for recognizing defect patterns of cold mill strip.
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.