Decision system for copper flotation backbone process

Abstract

This study proposed a decision system that can output the flotation backbone flowchart using the natural properties of copper ore. The proposed decision system includes three decision tasks: product scheme, flotation scheme and grinding scheme. Each decision task is a multi-label classification problem. To improve the classification effect of each sub-label, extreme gradient boosting (XGBoost) is used as a subclassifier, because of its ability to deal with small and high-dimensional samples. To selectively utilize the relations between the sub-labels in the same task, a modified classifier chain (MCC) was proposed. To specifically use the effect of a front-end task on a back-end task, the decision system connects the MCC-XGBoost corresponding to the three tasks in series. Accordingly, the outputs of a front-end task becomes the candidate features of a back-end task. To improve the recall rates of minority classes, the classification thresholds are customized using the Yoden index. Finally, the high performance of the decision system was demonstrated by hypothesis testing.