Research Article
Application of Machine Learning Techniques to Classify Intention to Pay for Forest Ecosystem Services
@INPROCEEDINGS{10.1007/978-3-031-58878-5_4, author={Pham Thu Thuy and Nguyen Thanh Tung and Luu Quoc Dat}, title={Application of Machine Learning Techniques to Classify Intention to Pay for Forest Ecosystem Services}, proceedings={Context-Aware Systems and Applications. 12th EAI International Conference, ICCASA 2023, Ho Chi Minh City, Vietnam, October 26-27, 2023, Proceedings}, proceedings_a={ICCASA}, year={2024}, month={8}, keywords={Intention to pay forest ecosystem services classification machine learning}, doi={10.1007/978-3-031-58878-5_4} }- Pham Thu Thuy
Nguyen Thanh Tung
Luu Quoc Dat
Year: 2024
Application of Machine Learning Techniques to Classify Intention to Pay for Forest Ecosystem Services
ICCASA
Springer
DOI: 10.1007/978-3-031-58878-5_4
Abstract
Capturing the ability to take part in the payment of forest ecosystem services by beneficiaries is the result that policy-making agencies are always concerned. This research selects several machine learning techniques, including single classifiers (Multilayer Perceptron, Naive Bayes, SMO) and ensemble classifiers (LogitBoost, Random Forest, Bagging) to evaluate and classify willingness-to-pay intention for mangrove ecosystem services of people in PhuLong commune, Vietnam. Research data is inherited from a previous contingent valuation survey, with a sample size of 235. The results show that the machine learning algorithms are workt with small sample-size data sets with feasibility prediction results in behavioral intent classification. The LogitBoost model achieves the best classification performance compared to the remaining models. Besides, socio-psychological factors are ranked as important factors in classifying behavioral intentions related to payment for forest ecosystem services.
