Dynamic global feature extraction and importance-correlation selection for the prediction of concentrate copper grade and recovery rate

Large-scale industrial data have brought great challenges to data calculation and analysis. Feature extraction and selection have become one of the research emphases in data mining. To mine the dynamic characteristics of large-scale industrial data, a dynamic global feature extraction (DGFE) method integrating principal component analysis (PCA) and kernel principal component analysis (KPCA) is proposed such that the achieved feature set is not only dynamic but also contains linear and non-linear features. To ensure that the obtained feature set is optimal with the minimum redundancy, a new importance-correlation-based feature selection (ICFS) method is proposed. To verify the validity and feasibility of the proposed methods, the partial least square (PLS) and least square support vector machine (LSSVM) prediction models for the concentrate copper grade and the recovery rate are established. The effectiveness of the proposed methods is verified through data experiments on a copper flotation industrial process.

Automated summary

In this paper, a dynamic global feature extraction (DGFE) method combining principal component analysis (PCA) and kernel principal component analysis (KPCA) is proposed to mine the dynamic characteristics of large-scale industrial data. Additionally, a new importance-correlation-based feature selection (ICFS) method is introduced to ensure the optimality of the obtained feature set. Experimental results on a copper flotation industrial process demonstrate the effectiveness of the proposed methods.