Molten steel temperature prediction model based on bootstrap Feature Subsets Ensemble Regression Trees
作者：
Highlights：
• Large-scale and noise data impose strong restrictions on building temperature models.
• To solve these two issues, the BFSE-RTs method is proposed in this paper.
• First, feature subsets are constructed based on multivariate fuzzy Taylor theorem.
• Second, smaller-scale and lower-dimensional bootstrap replications are used.
• Third, considering its simplicity, an RT is built on replications of each feature subset.
摘要
•Large-scale and noise data impose strong restrictions on building temperature models.•To solve these two issues, the BFSE-RTs method is proposed in this paper.•First, feature subsets are constructed based on multivariate fuzzy Taylor theorem.•Second, smaller-scale and lower-dimensional bootstrap replications are used.•Third, considering its simplicity, an RT is built on replications of each feature subset.
论文关键词：Ladle furnace,Molten steel temperature prediction,Large-scale data and noise data,Ensemble method
论文评审过程：Received 28 April 2015, Revised 24 February 2016, Accepted 25 February 2016, Available online 19 March 2016, Version of Record 16 April 2016.
论文官网地址：https://doi.org/10.1016/j.knosys.2016.02.018
