LAUSR

The accurate prediction of molten steel temperature is of great significance to the control of tapping temperature in ladle furnace. The more accurate the prediction is the better performance the controller will attain. In order to further improve the accuracy of existing data-driven predictive models, we propose a dynamic ensemble for regression to predict molten steel temperature. In contrast to existing ensemble models, we only select one base model with the highest competence from the pool for each test pattern, rather than fusing all base models. We can thus alleviate the effect of weak base models on the ensemble. Specifically, the operation of dynamic selection can be implemented by estimating the competence of all base models in the region of competence. In addition, the proposed ensemble generation method has taken into account the diversity and accuracy, which are crucial factors of deriving better ensembles. In order to investigate the effectiveness of our predictor, we compare it with several competitors proposed in literature on a real-world dataset. The experimental result has approved the superiority of our predictor.