Analyzing flight delay prediction under concept drift

Giusti, Lucas; Carvalho, Leonardo; Gomes, Antônio Tadeu A.; Coutinho, Rafaelli; Soares, Jorge de Abreu; Ogasawara, Eduardo

doi:10.1007/s12530-021-09415-z

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…Yi et al [17] studied the ML algorithm selection in flight delay prediction and proved that the stacking algorithm was better for flight delay prediction, which improved the classification accuracy and ensured greater stability. Giusti et al [18] studied the impact of concept drift processing strategies under different flight delay thresholds on flight delay prediction performance of learning models and realized a more robust classifier. Chen et al [19] proposed an early warning method of flight delay using weighted margin fuzzy SVM.…”

Section: Related Workmentioning

confidence: 99%

A sparrow search algorithm-optimized convolutional neural network for imbalanced data classification using synthetic minority over-sampling technique

Deng,

He,

Zhou

et al. 2023

Phys. Scr.

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Due to the large span of flight delay, numerous influencing factors and large number of flights, the collected flight delay data is characterized by large-scale and imbalance, which is considered as one of the largest challenges in balanced flight delay data classification by using conventional classification algorithms. To solve this problem, an adaptive multi-class classification approach of imbalanced flight delay data using synthetic minority over-sampling technique (SMOTE)-based convolutional neural network with sparrow search algorithm, namely SSA-LTCNN is proposed in this paper. Firstly, the SMOTE is used to reduce the imbalance between the classes of the flight delay data. Then, the qualitative analysis method is employed to determine the key parameters of the CNN, which are optimized by using SSA to establish an optimized CNN model. Finally, the optimized CNN model with more effective feature representation ability is employed to extract discriminative features from the balanced flight delay data automatically. The obtained features in the last fully connected layer of the optimized CNN model are used for flight delay classification. The MIT-BIH Arrhythmia Data is selected to verify the effectiveness of the proposed SSA-LTCNN method and the confusion matrix, classification accuracy, precision, recall and F1 score are also used to evaluate the performance of the proposed SSA-LTCNN method, which can get better performance than RF, DT, LR, CNN and QRSCNN. The actual flight delay data is processed to verify the effectiveness of the proposed classification approach, which can achieve higher accuracy than KNN, NB, UCNN, CNN, SSA-LCNN and SSA-TCNN in different time periods. The experimental results show that the SSA-LTCNN can fast and effectively realize the classification of MIT-BIH Arrhythmia Data and flight delay data.

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Section: Related Workmentioning

confidence: 99%

A sparrow search algorithm-optimized convolutional neural network for imbalanced data classification using synthetic minority over-sampling technique

Deng,

He,

Zhou

et al. 2023

Phys. Scr.

View full text Add to dashboard Cite

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Social ski driver conditional autoregressive-based deep learning classifier for flight delay prediction

Bisandu

Moulitsas

Filippone

2022

Neural Comput & Applic

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The importance of robust flight delay prediction has recently increased in the air transportation industry. This industry seeks alternative methods and technologies for more robust flight delay prediction because of its significance for all stakeholders. The most affected are airlines that suffer from monetary and passenger loyalty losses. Several studies have attempted to analysed and solve flight delay prediction problems using machine learning methods. This research proposes a novel alternative method, namely social ski driver conditional autoregressive-based (SSDCA-based) deep learning. Our proposed method combines the Social Ski Driver algorithm with Conditional Autoregressive Value at Risk by Regression Quantiles. We consider the most relevant instances from the training dataset, which are the delayed flights. We applied data transformation to stabilise the data variance using Yeo-Johnson. We then perform the training and testing of our data using deep recurrent neural network (DRNN) and SSDCA-based algorithms. The SSDCA-based optimisation algorithm helped us choose the right network architecture with better accuracy and less error than the existing literature. The results of our proposed SSDCA-based method and existing benchmark methods were compared. The efficiency and computational time of our proposed method are compared against the existing benchmark methods. The SSDCA-based DRNN provides a more accurate flight delay prediction with 0.9361 and 0.9252 accuracy rates on both dataset-1 and dataset-2, respectively. To show the reliability of our method, we compared it with other meta-heuristic approaches. The result is that the SSDCA-based DRNN outperformed all existing benchmark methods tested in our experiment.

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Concept drift from 1980 to 2020: a comprehensive bibliometric analysis with future research insight

2023

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Analyzing flight delay prediction under concept drift

Cited by 4 publications

References 32 publications

A sparrow search algorithm-optimized convolutional neural network for imbalanced data classification using synthetic minority over-sampling technique

A sparrow search algorithm-optimized convolutional neural network for imbalanced data classification using synthetic minority over-sampling technique

Social ski driver conditional autoregressive-based deep learning classifier for flight delay prediction

Concept drift from 1980 to 2020: a comprehensive bibliometric analysis with future research insight

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