Correlation and Knowledge-Based Joint Feature Selection for Copper Flotation Backbone Process Design

The intelligentization of flotation design plays a crucial role in enhancing industrial competitiveness and resource efficiency. Our previous work established a mapping relationship between the flotation backbone process graph and label vectors, enabling an intelligent design of the copper flotation backbone process through multilabel classification. Due to the insufficient quantity of training samples in historical databases, traditional feature selection methods perform poorly, owing to insufficient learning. To address the label-specific feature selection problem for this design, this study proposes correlation and knowledge-based joint feature selection (CK-JFS). In this proposed method, label correlations ensure that features specific to strongly related labels are prioritized, while domain knowledge further refines the selection process by applying specialized knowledge to copper flotation. This mode of data and knowledge integration significantly reduces the reliance of label-specific feature selection on the number of training samples. The results demonstrate that CK-JFS achieves significantly higher accuracy and computational efficiency compared to traditional multilabel feature selection algorithms in the context of copper flotation backbone process design.