Design of a binary decision tree using the genetic algorithm and K-means algorithm for recognition of the defect patterns of cold mill strip

This paper suggests a method to recognize the various defect patterns of a cold mill strip using a binary decision tree. In classifying complex patterns with high similarity like the defect patterns of a cold mill strip, the selection of an optimal feature set and an appropriate recognizer is important to achieve high recognition rate. In this paper the GA (genetic algorithm) 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 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 a neural network learning sets of standard patterns at each node. In this paper, the classifier using the binary decision tree was 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.