A deep feedforward neural network and shallow architectures effectiveness comparison: Flight delays classification perspective

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dc.contributor.authorBisandu, Desmond Bala
dc.contributor.authorHomaid, Mohammed Salih A
dc.contributor.authorMoulitsas, Irene
dc.contributor.authorFilippone, Salvatore
dc.date.accessioned2022-01-28T12:59:21Z
dc.date.available2022-01-28T12:59:21Z
dc.date.issued2021-11-22
dc.description.abstractFlight delays have negatively impacted the socio-economics state of passengers, airlines and airports, resulting in huge economic losses. Hence, it has become necessary to correctly predict their occurrences in decision-making because it is important for the effective management of the aviation industry. Developing accurate flight delays classification models depends mostly on the air transportation system complexity and the infrastructure available in airports, which may be a region-specific issue. However, no specific prediction or classification model can handle the individual characteristics of all airlines and airports at the same time. Hence, the need to further develop and compare predictive models for the aviation decision system of the future cannot be over-emphasised. In this research, flight on-time data records from the United State Bureau of Transportation Statistics was employed to evaluate the performances of Deep Feedforward Neural Network, Neural Network, and Support Vector Machine models on a binary classification problem. The research revealed that the models achieved different accuracies of flight delay classifications. The Support Vector Machine had the worst average accuracy than Neural Network and Deep Feedforward Neural Network in the initial experiment. The Deep Feedforward Neural Network outperformed Support Vector Machines and Neural Network with the best average percentage accuracies. Going further to investigate the Deep Feedforward Neural Network architecture on different parameters against itself suggest that training a Deep Feedforward Neural Network algorithm, regardless of data training size, the classification accuracy peaks. We examine which number of epochs works best in our flight delay classification settings for the Deep Feedforward Neural Network. Our experiment results demonstrate that having many epochs affects the convergence rate of the model; unlike when hidden layers are increased, it does not ensure better or higher accuracy in a binary classification of flight delays. Finally, we recommended further studies on the applicability of the Deep Feedforward Neural Network in flight delays prediction with specific case studies of either airlines or airports to check the impact on the model’s performance.en_UK
dc.identifier.citationBisandu DB, Homaid MS, Moulitsas I, Filippone S. (2021) A deep feedforward neural network and shallow architectures effectiveness comparison: Flight delays classification perspective. In: 5th international conference on advances in artificial intelligence (ICAAI), 20-22 November 2021, London, UK, Virtual Eventen_UK
dc.identifier.isbn978-1-4503-9069-9
dc.identifier.urihttps://doi.org/10.1145/3505711.3505712
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17518
dc.language.isoenen_UK
dc.publisherACMen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectcomputing methodologiesen_UK
dc.subjectmachine learningen_UK
dc.subjectmachine learning approachesen_UK
dc.subjectneural networksen_UK
dc.titleA deep feedforward neural network and shallow architectures effectiveness comparison: Flight delays classification perspectiveen_UK
dc.typeConference paperen_UK

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