Machine-learning-assisted multi-objective optimization in vertical zone refining of ultra-high purity indium

•A multi-objective optimization strategy was designed by machine learning with regard to vertical zone refining of ultra-high purity indium.•Classification models were established for certain impurities (Si, S, Fe, Zn, Ni, Cu and As).•The Synthetic Minority Over-sampling Technique was introduced to overcome the sample imbalance problem of the experimental dataset.•A ridge regression model was built to predict the total impurity content in the product.•The processing parameters could be high-throughput virtually screened and experiments in turn validated the reliability of the models.