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Modelling Flight Delays in the Presence of Class Imbalance

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Sook-Ling ChuaAuthors Info & Claims

AICCC '18: Proceedings of the 2018 Artificial Intelligence and Cloud Computing Conference

Pages 186 - 191

https://doi.org/10.1145/3299819.3299847

Published: 21 December 2018 Publication History

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Abstract

Flight delay is one of the common problems faced by many air passengers. Delays in flights not only bring about inconvenience to passengers, but also cost the airlines. To streamline travel experience, airlines have been leveraging on data analytics to predict flight delays. Although many prediction models have been proposed, they perform poorly especially on data that have imbalanced class distributions. Often, these models pay less attention to the minority 'delay' class, which are usually more relevant and important. In this paper, we address the issue of imbalanced class distributions to improve the overall classification performance in predicting flight delays. We validated our approach on a public airline on-time performance dataset.

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Cited By

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Gilbert JHow C(2024)Model to Predict Flight Arrival Delay in John F. Kennedy Airport2024 International Conference on Future Technologies for Smart Society (ICFTSS)10.1109/ICFTSS61109.2024.10691358(32-36)Online publication date: 7-Aug-2024
https://doi.org/10.1109/ICFTSS61109.2024.10691358
Bala Bisandu DSalih Homaid MMoulitsas iFilippone S(2021)A Deep Feedforward Neural Network and Shallow Architectures Effectiveness Comparison: Flight Delays Classification PerspectiveProceedings of the 5th International Conference on Advances in Artificial Intelligence10.1145/3505711.3505712(1-10)Online publication date: 20-Nov-2021
https://dl.acm.org/doi/10.1145/3505711.3505712

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Modelling Flight Delays in the Presence of Class Imbalance

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Published In

AICCC '18: Proceedings of the 2018 Artificial Intelligence and Cloud Computing Conference

December 2018

206 pages

ISBN:9781450366236

DOI:10.1145/3299819

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 December 2018

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Ministry of Education, Malaysia

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AICCC '18

AICCC '18: 2018 Artificial Intelligence and Cloud Computing Conference

December 21 - 23, 2018

Tokyo, Japan

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Cited By

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Gilbert JHow C(2024)Model to Predict Flight Arrival Delay in John F. Kennedy Airport2024 International Conference on Future Technologies for Smart Society (ICFTSS)10.1109/ICFTSS61109.2024.10691358(32-36)Online publication date: 7-Aug-2024
https://doi.org/10.1109/ICFTSS61109.2024.10691358
Bala Bisandu DSalih Homaid MMoulitsas iFilippone S(2021)A Deep Feedforward Neural Network and Shallow Architectures Effectiveness Comparison: Flight Delays Classification PerspectiveProceedings of the 5th International Conference on Advances in Artificial Intelligence10.1145/3505711.3505712(1-10)Online publication date: 20-Nov-2021
https://dl.acm.org/doi/10.1145/3505711.3505712

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Flight Delay Prediction using Airport Situational Awareness Map

A Novel Distribution Analysis for SMOTE Oversampling Method in Handling Class Imbalance

A Re-sampling Method for Class Imbalance Learning with Credit Data