The intelligent decision-making of copper flotation backbone process based on CK-XGBoost

Abstract

This study creatively puts forward XGBoosts with correlation-based and knowledge-based function (CK-XGBoost) to design flotation backbone process according to the natural properties of a copper mine. The decision-making of flotation backbone process of copper mine is a multi label problem with high feature dimension and small instance set. The proposed CK-XGBoost selects label-specific features and trains a binary XGBoost for each label, and the information of other labels is used to assist the classifier modeling through correlation-based function and knowledge-based function. The correlation-based function utilizes the Pearson correlation coefficient between labels in training set, so that the predicted values of two strong positive correlation labels are similar and the predicted values of two strong negative correlation labels are contrary. The knowledge-based function utilizes domain knowledge, which is independent of the distribution of training set. The experimental results demonstrate the significantly superiority of the proposed CK-XGBoost in the decision-making of copper flotation backbone process.