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A Novel Feature Selection Technique to Better Predict Climate Change Stage of Change

Author

Listed:
  • Hamed Naseri

    (Department of Civil, Geological, and Mining Engineering, Polytechnique Montréal, Montreal, QC H3T 1J4, Canada)

  • E. Owen D. Waygood

    (Department of Civil, Geological, and Mining Engineering, Polytechnique Montréal, Montreal, QC H3T 1J4, Canada)

  • Bobin Wang

    (Department of Mechanical Engineering, Université Laval, Quebec, QC G1V 0A6, Canada)

  • Zachary Patterson

    (Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC H3G 1M8, Canada)

  • Ricardo A. Daziano

    (School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA)

Abstract

Indications of people’s environmental concern are linked to transport decisions and can provide great support for policymaking on climate change. This study aims to better predict individual climate change stage of change (CC-SoC) based on different features of transport-related behavior, General Ecological Behavior, New Environmental Paradigm, and socio-demographic characteristics. Together these sources result in over 100 possible features that indicate someone’s level of environmental concern. Such a large number of features may create several analytical problems, such as overfitting, accuracy reduction, and high computational costs. To this end, a new feature selection technique, named the Coyote Optimization Algorithm-Quadratic Discriminant Analysis (COA-QDA), is first proposed to find the optimal features to predict CC-SoC with the highest accuracy. Different conventional feature selection methods (Lasso, Elastic Net, Random Forest Feature Selection, Extra Trees, and Principal Component Analysis Feature Selection) are employed to compare with the COA-QDA. Afterward, eight classification techniques are applied to solve the prediction problem. Finally, a sensitivity analysis is performed to determine the most important features affecting the prediction of CC-SoC. The results indicate that COA-QDA outperforms conventional feature selection methods by increasing average testing data accuracy from 0.7% to 5.6%. Logistic Regression surpasses other classifiers with the highest prediction accuracy.

Suggested Citation

  • Hamed Naseri & E. Owen D. Waygood & Bobin Wang & Zachary Patterson & Ricardo A. Daziano, 2021. "A Novel Feature Selection Technique to Better Predict Climate Change Stage of Change," Sustainability, MDPI, vol. 14(1), pages 1-23, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2021:i:1:p:40-:d:707732
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    References listed on IDEAS

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    1. Susilo, Yusak O. & Williams, Katie & Lindsay, Morag & Dair, Carol, 2012. "The influence of individuals’ environmental attitudes and urban design features on their travel patterns in sustainable neighborhoods in the UK," Working papers in Transport Economics 2012:1, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
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    1. Hamed Naseri & Edward Owen Douglas Waygood & Bobin Wang & Zachary Patterson, 2022. "Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters," IJERPH, MDPI, vol. 19(24), pages 1-19, December.

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