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An Efficient Elastic Net with Regression Coefficients Method for Variable Selection of Spectrum Data

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  • Wenya Liu
  • Qi Li

Abstract

Using the spectrum data for quality prediction always suffers from noise and colinearity, so variable selection method plays an important role to deal with spectrum data. An efficient elastic net with regression coefficients method (Enet-BETA) is proposed to select the significant variables of the spectrum data in this paper. The proposed Enet-BETA method can not only select important variables to make the quality easy to interpret, but also can improve the stability and feasibility of the built model. Enet-BETA method is not prone to overfitting because of the reduction of redundant variables realized by elastic net method. Hypothesis testing is used to further simplify the model and provide a better insight into the nature of process. The experimental results prove that the proposed Enet-BETA method outperforms the other methods in terms of prediction performance and model interpretation.

Suggested Citation

  • Wenya Liu & Qi Li, 2017. "An Efficient Elastic Net with Regression Coefficients Method for Variable Selection of Spectrum Data," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-13, February.
    • Handle: RePEc:plo:pone00:0171122
    DOI: 10.1371/journal.pone.0171122
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    References listed on IDEAS

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    1. Wang, Xiaoming & Park, Taesung & Carriere, K.C., 2010. "Variable selection via combined penalization for high-dimensional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2230-2243, October.
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    Cited by:

    1. Kimon Ntotsis & Alex Karagrigoriou & Andreas Artemiou, 2021. "Interdependency Pattern Recognition in Econometrics: A Penalized Regularization Antidote," Econometrics, MDPI, vol. 9(4), pages 1-13, December.
    2. Belaïd, Fateh & Roubaud, David & Galariotis, Emilios, 2019. "Features of residential energy consumption: Evidence from France using an innovative multilevel modelling approach," Energy Policy, Elsevier, vol. 125(C), pages 277-285.
    3. Soyoung Park & Jinsoo Kim, 2021. "The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential," Sustainability, MDPI, vol. 13(5), pages 1-19, February.
    4. Khan, Faridoon & Muhammadullah, Sara & Sharif, Arshian & Lee, Chien-Chiang, 2024. "The role of green energy stock market in forecasting China's crude oil market: An application of IIS approach and sparse regression models," Energy Economics, Elsevier, vol. 130(C).
    5. Indy Man Kit Ho & Kai Yuen Cheong & Anthony Weldon, 2021. "Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-27, April.

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