Mohammed, Z, Asghar, M and Kanwal, N ORCID: https://orcid.org/0000-0002-9732-3126 (2021) Analyzing the impact of COVID-19 on flight cancellation using machine learning and deep learning algorithms for a highly unbalanced dataset. In: 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), 09-10 December 2021, Cape Town, South Africa.

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Abstract

Flight cancellations can be caused by many factors, including adverse weather conditions, and can result in lost money and time, etc. The COVID-19 pandemic has significantly exacerbated this situation, leading to a significant decrease in air travel. In 2020, the number of cancelled flights increased by 200% over 2019 and the number of flights decreased by 38%. This research focused on analyzing the impact of COVID-19 on flight cancellation using publicly available datasets from different locations. We looked further into the impact of class imbalance and techniques to reduce its effects on classification errors. The research was performed using four data sets, six re-sampling techniques, and 12 modeling algorithms. Random oversampling combined with random subsampling outperformed all other resampling techniques and multi-layer perceptron (MLP) was the best among all other machine learning models. For validation, we used the same resampling technique to two additional datasets namely income and diabetes datasets. The results showed that combining random oversampling with subsampling improved the accuracy of machine learning models.

Item Type: Conference or Workshop Item (Paper)
Additional Information: All final information related to this published conference article, including copyrights, can be found on the publisher website.
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Depositing User: Symplectic
Date Deposited: 22 Jul 2022 07:53
Last Modified: 22 Jul 2022 07:53
URI: https://eprints.keele.ac.uk/id/eprint/11072

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