Predicting Green Consumption Behaviors of Students Using Efficient Firefly Grey Wolf-Assisted K-Nearest Neighbor Classifiers
Understanding the green consumption behaviors of college students is highly demanded to update the public and educational policies of universities. For this purpose, this research is devoted to advance an efficient model for identifying prominent features and predicting the green consumption behavio...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8998214/ |
| _version_ | 1818428862044831744 |
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| author | Hua Tang Yueting Xu Aiju Lin Ali Asghar Heidari Mingjing Wang Huiling Chen Yungang Luo Chengye Li |
| author_facet | Hua Tang Yueting Xu Aiju Lin Ali Asghar Heidari Mingjing Wang Huiling Chen Yungang Luo Chengye Li |
| author_sort | Hua Tang |
| collection | DOAJ |
| description | Understanding the green consumption behaviors of college students is highly demanded to update the public and educational policies of universities. For this purpose, this research is devoted to advance an efficient model for identifying prominent features and predicting the green consumption behaviors of college students. The proposed prediction model is based on the K-Nearest Neighbor (KNN) with an effective swarm intelligence method, which is called OBLFA_GWO. The optimization core takes advantage of the firefly algorithm (FA) and opposition-based learning (OBL) to mitigate the immature convergence of the grey wolf algorithm (GWO). In the proposed prediction framework, OBLFA_GWO is utilized to identify influential features. Then, the enhanced KNN model is used to identify the importance and interrelationships of features in samples and construct an effective and stable predictive model for decision support. Five other well-known algorithms are employed to validate the effectiveness of the proposed OBLFA_GWO strategy using 13 benchmark test problems. Also, the non-parametric statistical Wilcoxon sign rank and Friedman tests are conducted to validate the significance of the proposed OBLFA_GWO against other peers. Experimental results indicate that the FA and OBL can significantly boost the core exploratory and exploitative trends of GWO in dealing with the optimization tasks. Also, the OBLFA_GWO-based KNN (OBLFA_GWO-KNN) model is compared with four classical classifiers, such as kernel extreme learning machine (KELM), backpropagation neural network method (BPNN), and random forest (RF) and five advanced feature selection methods in terms of four standard evaluation indexes. The experimental results show that the classification accuracy of the proposed OBLFA_GWO-KNN can reach to 96.334 % on the real-life dataset collected from nine universities. Also, the proposed binary OBLFA_GWO algorithm has improved the classification performance of KNN compared to the other peers. Hopefully, the established adaptive OBLFA_GWO-KNN model can be considered as a useful tool for predicting students' behavior of green consumption. |
| first_indexed | 2024-12-14T15:08:21Z |
| format | Article |
| id | doaj.art-f334d01969ad4e20bf5087f212eec3da |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T15:08:21Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f334d01969ad4e20bf5087f212eec3da2022-12-21T22:56:38ZengIEEEIEEE Access2169-35362020-01-018355463556210.1109/ACCESS.2020.29737638998214Predicting Green Consumption Behaviors of Students Using Efficient Firefly Grey Wolf-Assisted K-Nearest Neighbor ClassifiersHua Tang0Yueting Xu1https://orcid.org/0000-0001-8125-5942Aiju Lin2Ali Asghar Heidari3https://orcid.org/0000-0001-6938-9948Mingjing Wang4https://orcid.org/0000-0003-1985-4076Huiling Chen5https://orcid.org/0000-0002-7714-9693Yungang Luo6Chengye Li7School of Business, Wenzhou University, Wenzhou, ChinaCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, ChinaCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, ChinaSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, IranInstitute of Research and Development, Duy Tan University, Da Nang, VietnamCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, ChinaDepartment of Stomatology, The Second Hospital of Jilin University, Changchun, ChinaDepartment of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaUnderstanding the green consumption behaviors of college students is highly demanded to update the public and educational policies of universities. For this purpose, this research is devoted to advance an efficient model for identifying prominent features and predicting the green consumption behaviors of college students. The proposed prediction model is based on the K-Nearest Neighbor (KNN) with an effective swarm intelligence method, which is called OBLFA_GWO. The optimization core takes advantage of the firefly algorithm (FA) and opposition-based learning (OBL) to mitigate the immature convergence of the grey wolf algorithm (GWO). In the proposed prediction framework, OBLFA_GWO is utilized to identify influential features. Then, the enhanced KNN model is used to identify the importance and interrelationships of features in samples and construct an effective and stable predictive model for decision support. Five other well-known algorithms are employed to validate the effectiveness of the proposed OBLFA_GWO strategy using 13 benchmark test problems. Also, the non-parametric statistical Wilcoxon sign rank and Friedman tests are conducted to validate the significance of the proposed OBLFA_GWO against other peers. Experimental results indicate that the FA and OBL can significantly boost the core exploratory and exploitative trends of GWO in dealing with the optimization tasks. Also, the OBLFA_GWO-based KNN (OBLFA_GWO-KNN) model is compared with four classical classifiers, such as kernel extreme learning machine (KELM), backpropagation neural network method (BPNN), and random forest (RF) and five advanced feature selection methods in terms of four standard evaluation indexes. The experimental results show that the classification accuracy of the proposed OBLFA_GWO-KNN can reach to 96.334 % on the real-life dataset collected from nine universities. Also, the proposed binary OBLFA_GWO algorithm has improved the classification performance of KNN compared to the other peers. Hopefully, the established adaptive OBLFA_GWO-KNN model can be considered as a useful tool for predicting students' behavior of green consumption.https://ieeexplore.ieee.org/document/8998214/K-nearest neighborfirefly algorithmgrey wolf algorithmopposition-based learninggreen consumption behaviorfeature selection |
| spellingShingle | Hua Tang Yueting Xu Aiju Lin Ali Asghar Heidari Mingjing Wang Huiling Chen Yungang Luo Chengye Li Predicting Green Consumption Behaviors of Students Using Efficient Firefly Grey Wolf-Assisted K-Nearest Neighbor Classifiers IEEE Access K-nearest neighbor firefly algorithm grey wolf algorithm opposition-based learning green consumption behavior feature selection |
| title | Predicting Green Consumption Behaviors of Students Using Efficient Firefly Grey Wolf-Assisted K-Nearest Neighbor Classifiers |
| title_full | Predicting Green Consumption Behaviors of Students Using Efficient Firefly Grey Wolf-Assisted K-Nearest Neighbor Classifiers |
| title_fullStr | Predicting Green Consumption Behaviors of Students Using Efficient Firefly Grey Wolf-Assisted K-Nearest Neighbor Classifiers |
| title_full_unstemmed | Predicting Green Consumption Behaviors of Students Using Efficient Firefly Grey Wolf-Assisted K-Nearest Neighbor Classifiers |
| title_short | Predicting Green Consumption Behaviors of Students Using Efficient Firefly Grey Wolf-Assisted K-Nearest Neighbor Classifiers |
| title_sort | predicting green consumption behaviors of students using efficient firefly grey wolf assisted k nearest neighbor classifiers |
| topic | K-nearest neighbor firefly algorithm grey wolf algorithm opposition-based learning green consumption behavior feature selection |
| url | https://ieeexplore.ieee.org/document/8998214/ |
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