A Two-Stage Regularization Method for Variable Selection and Forecasting in High-Order Interaction Model
Joint Authors
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-11-11
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Forecasting models with high-order interaction has become popular in many applications since researchers gradually notice that an additive linear model is not adequate for accurate forecasting.
However, the excessive number of variables with low sample size in the model poses critically challenges to predication accuracy.
To enhance the forecasting accuracy and training speed simultaneously, an interpretable model is essential in knowledge recovery.
To deal with ultra-high dimensionality, this paper investigates and studies a two-stage procedure to demand sparsity within high-order interaction model.
In each stage, square root hard ridge (SRHR) method is applied to discover the relevant variables.
The application of square root loss function facilitates the parameter tuning work.
On the other hand, hard ridge penalty function is able to handle both the high multicollinearity and selection inconsistency.
The real data experiments reveal the superior performances to other comparing approaches.
American Psychological Association (APA)
Dong, Yao& Jiang, He. 2018. A Two-Stage Regularization Method for Variable Selection and Forecasting in High-Order Interaction Model. Complexity،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1133116
Modern Language Association (MLA)
Dong, Yao& Jiang, He. A Two-Stage Regularization Method for Variable Selection and Forecasting in High-Order Interaction Model. Complexity No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1133116
American Medical Association (AMA)
Dong, Yao& Jiang, He. A Two-Stage Regularization Method for Variable Selection and Forecasting in High-Order Interaction Model. Complexity. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1133116
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1133116