文章基本信息

标题：A Novel Two-step Sparse Learning Approach for Variable Selection and Optimal Predictive Modeling
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作者：Yiren Liu ; S. Joe Qin
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：7
页码：57-64
DOI：10.1016/j.ifacol.2022.07.422
语种：English
出版社：Elsevier
摘要：AbstractIn this paper, a two-step sparse learning approach is proposed for variable selection and model parameter estimation with optimally tuned hyperparameters in each step. In Step one, a sparse learning algorithm is applied on all data to produce a sequence of candidate subsets of selected variables by varying the hyperparameter value. In Step two, for each subset of the selected variables from Step one, Lasso, ridge regression, elastic-net, or adaptive Lasso is employed to find the optimal hyperparameters with the best cross-validation error. Among all subsets, the one with the overall minimum cross-validation error is selected as globally optimal. The effectiveness of the proposed approach is demonstrated using an industrial NOx emission dataset and the Dow challenge dataset to predict product impurity.
关键词：KeywordsInferential modelingsparse statistical learningvariable selectionregularizationindustrial applications