Abstract:In the separation of scrap metal based on dual-energy X-ray transmission technology, the recognition effect is largely affected by the characteristics of the selected materials. The features used in the existing scrap metal material recognition algorithms are not comprehensive enough, and the redundancy among feature data is large, so the recognition accuracy is not high. To solve this problem, this paper gives full consideration to the redundancy between features and the correlation between features and categories, and puts forward a Correlation-based Feature Selection method (CFS) to determine the optimal feature subset consisting of featuresIH、IL、x、TML and TMH among many material features. Comparative experiments were carried out by using different material feature combination methods, and K Nearest Neighbor (KNN) under the optimal feature subset was selected as the optimal classification model. The experimental results show that: compared with the existing classification methods, the identification and classification method of waste nonferrous metals materials based on the Correlation-based Feature Selection and K Nearest Neighbor (CFS-KNN) has higher identification accuracy (96.13%) with relatively few features.