Boosting the prediction of molten steel temperature in ladle furnace with a dynamic outlier ensemble

Molten steel temperature prediction is a critical step in the development of level-two control systems for ladle furnace. Many machine learning algorithms have been employed to complete such a work. Whereas data-driven predictors often deteriorate due to the presence of outliers in practical applications. This paper proposes to boost the predictive performance via outlier detection. Specifically, a dynamic outlier ensemble is developed inspired by the superiority of dynamic classifier selection in classification. Clustering analysis is used to determine the region of competence, on which base detectors are selected with the dedicated measure. The reason for the usage of clustering analysis lies in its efficiency during online detection. One attribute weighting algorithm is used to enhance the capability of clustering in outlier detection. The information behind regression is used to facilitate the measure of competence, results of which can promote the performance of predictors. Such a strategy can achieve double-win from the perspective of regression and outlier detection. Extensive experiments on real-world data sets show that results of all 4 predictive models with respect to accuracy and hit rate can be improved. Moreover, the detection performance in terms of G-mean and F1 score of our detector has also been confirmed via the comparison with 8 competitors.

Automated summary

This paper proposes a method to improve the predictive performance of molten steel temperature by outlier detection. The method utilizes dynamic outlier ensemble and clustering analysis to select the best base detectors, thus promoting the performance of predictive models.