Flight Delay Classification Prediction Based on Stacking Algorithm
With the development of civil aviation, the number of flights keeps increasing and the flight delay has become a serious issue and even tends to normality. This paper aims to prove that Stacking algorithm has advantages in airport flight delay prediction, especially for the algorithm selection probl...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/4292778 |
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| _version_ | 1832550901592621056 |
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| author | Jia Yi Honghai Zhang Hao Liu Gang Zhong Guiyi Li |
| author_facet | Jia Yi Honghai Zhang Hao Liu Gang Zhong Guiyi Li |
| author_sort | Jia Yi |
| collection | DOAJ |
| description | With the development of civil aviation, the number of flights keeps increasing and the flight delay has become a serious issue and even tends to normality. This paper aims to prove that Stacking algorithm has advantages in airport flight delay prediction, especially for the algorithm selection problem of machine learning technology. In this research, the principle of the Stacking classification algorithm is introduced, the SMOTE algorithm is selected to process imbalanced datasets, and the Boruta algorithm is utilized for feature selection. There are five supervised machine learning algorithms in the first-level learner of Stacking including KNN, Random Forest, Logistic Regression, Decision Tree, and Gaussian Naive Bayes. The second-level learner is Logistic Regression. To verify the effectiveness of the proposed method, comparative experiments are carried out based on Boston Logan International Airport flight datasets from January to December 2019. Multiple indexes are used to comprehensively evaluate the prediction results, such as Accuracy, Precision, Recall, F1 Score, ROC curve, and AUC Score. The results show that the Stacking algorithm not only could improve the prediction accuracy but also maintains great stability. |
| format | Article |
| id | doaj-art-4ea8d3362f0b4da78b96709c01ecc2cd |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-4ea8d3362f0b4da78b96709c01ecc2cd2025-02-03T06:05:33ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/42927784292778Flight Delay Classification Prediction Based on Stacking AlgorithmJia Yi0Honghai Zhang1Hao Liu2Gang Zhong3Guiyi Li4College of Civil Aviation, Nanjing University of Aeronautics&Astronautics, Nanjing 211106, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics&Astronautics, Nanjing 211106, ChinaCollege of Science, Nanjing University of Aeronautics&Astronautics, Nanjing 211106, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics&Astronautics, Nanjing 211106, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics&Astronautics, Nanjing 211106, ChinaWith the development of civil aviation, the number of flights keeps increasing and the flight delay has become a serious issue and even tends to normality. This paper aims to prove that Stacking algorithm has advantages in airport flight delay prediction, especially for the algorithm selection problem of machine learning technology. In this research, the principle of the Stacking classification algorithm is introduced, the SMOTE algorithm is selected to process imbalanced datasets, and the Boruta algorithm is utilized for feature selection. There are five supervised machine learning algorithms in the first-level learner of Stacking including KNN, Random Forest, Logistic Regression, Decision Tree, and Gaussian Naive Bayes. The second-level learner is Logistic Regression. To verify the effectiveness of the proposed method, comparative experiments are carried out based on Boston Logan International Airport flight datasets from January to December 2019. Multiple indexes are used to comprehensively evaluate the prediction results, such as Accuracy, Precision, Recall, F1 Score, ROC curve, and AUC Score. The results show that the Stacking algorithm not only could improve the prediction accuracy but also maintains great stability.http://dx.doi.org/10.1155/2021/4292778 |
| spellingShingle | Jia Yi Honghai Zhang Hao Liu Gang Zhong Guiyi Li Flight Delay Classification Prediction Based on Stacking Algorithm Journal of Advanced Transportation |
| title | Flight Delay Classification Prediction Based on Stacking Algorithm |
| title_full | Flight Delay Classification Prediction Based on Stacking Algorithm |
| title_fullStr | Flight Delay Classification Prediction Based on Stacking Algorithm |
| title_full_unstemmed | Flight Delay Classification Prediction Based on Stacking Algorithm |
| title_short | Flight Delay Classification Prediction Based on Stacking Algorithm |
| title_sort | flight delay classification prediction based on stacking algorithm |
| url | http://dx.doi.org/10.1155/2021/4292778 |
| work_keys_str_mv | AT jiayi flightdelayclassificationpredictionbasedonstackingalgorithm AT honghaizhang flightdelayclassificationpredictionbasedonstackingalgorithm AT haoliu flightdelayclassificationpredictionbasedonstackingalgorithm AT gangzhong flightdelayclassificationpredictionbasedonstackingalgorithm AT guiyili flightdelayclassificationpredictionbasedonstackingalgorithm |