Boosting the prediction of molten steel temperature in ladle furnace with a dynamic outlier ensemble. (November 2022)

• Record Type: Journal Article
• Authors: Wang, Biao; Wang, Wenjing; Meng, Guanglei; Qiao, Zhihua; Guo, Yuming; Wang, Na; Wang, Wei; Mao, Zhizhong
• Is Part Of: Engineering applications of artificial intelligence. Volume 116(2022)
• Publication Date: 2022-11
• Subjects: Outlier detection -- Ensemble learning -- Dynamic ensemble -- Ladle furnace; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105359
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 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.