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

Hamed Naseri, E. Owen D. Waygood, Bobin Wang, Zachary Patterson and Ricardo A. Daziano
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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

Sustainability, 2021, vol. 14, issue 1, 1-23

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.

Keywords: climate change stage of change; feature selection; transport-related behavior; optimization; classification (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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