Binary Decision Tree Using Genetic Algorithm for Recognizing Defect Patterns of Cold Mill Strip

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.