Comparing SVM and GLM in Calculating Insurance Premium for Flight Delay

Lukas, Samuel; Stefani, Dina; Widjaja, Petrus

doi:10.1145/3369114.3369160

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…A Deep Feedforward Neural Network is a network consisting of at least an input layer, two or more hidden layers and an output layer. Several functions are attached to DNNs [29,40]For instance, we might have five functions f (1) , f (2) , f (3) , f (4) and f (5) all in a chain connection forming a function f(x) ≈ (f (5) , f (4) , f (3) , f (2) , f (1) (x)). While in this case f (1) is known as the first layer, f (2) is known as the second layer, and it continues until the final layer which is called the output layer.…”

Section: Deep Feedforward Neural Networkmentioning

confidence: 99%

A Deep Feedforward Neural Network and Shallow Architectures Effectiveness Comparison: Flight Delays Classification Perspective

Bisandu

Homaid

Moulitsas

et al. 2021

2021 the 5th International Conference on Advances in Artificial Intelligence (ICAAI)

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Flight 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 the process of 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 aviation decision system of the future cannot be over-emphasized. 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 different accuracies of flight delay classifications were achieved by the models. 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 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 Deep Feedforward Neural Network in flight delays prediction with specific case studies of either airlines or airports to check the impact on the performance of the model.

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Section: Deep Feedforward Neural Networkmentioning

confidence: 99%

A Deep Feedforward Neural Network and Shallow Architectures Effectiveness Comparison: Flight Delays Classification Perspective

Bisandu

Homaid

Moulitsas

et al. 2021

2021 the 5th International Conference on Advances in Artificial Intelligence (ICAAI)

View full text Add to dashboard Cite

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“…To help the service providers, many scientists are working on adjusting the CPS or insurance fee based on airlines and origin-destination pairs. In the work of Samuel Lukas et al (Lukas et al 2019), they use standard deviation, Generalize Linear Model, and Support Vector machine to calculate the premium, which will be included in the ticket price and help the airline's company to improve their service and cover the cost of compensation. An insurance premium model for case delay or cancellation is proposed from Stefani et al (2019), the author found that the calculation works better when considering the airline and the departure city of the flight.…”

Section: Introductionmentioning

confidence: 99%

The personalized recommendation for OTA flight cancellation and change services during the pandemic

Gong¹,

Wang²,

Nie³

et al. 2022

J Revenue Pricing Manag

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The COVID-19 pandemic has had a dramatic impact on people's travels. Due to the recurrent pandemic and regionally different policies in China, travelers must pay a lot for flight cancellations and changes. To accommodate this, online travel agencies (OTA) can provide a more flexible ancillary as a supplement to the airline company's services. Here, we introduced the upgraded all-in-one (AIO) service package, which offers compensation for flight delays, changes, or refund. We also designed a dynamic recommendation engine (DRE), which can make real-time personalized recommendations. Backed by AB testing, the machine learning-based DRE not only raises the package attach rate without interrupting the flight ordering process, but also helps the customers cut cost when making flight cancellations or changes.

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Comparing SVM and GLM in Calculating Insurance Premium for Flight Delay

Cited by 2 publications

References 5 publications

A Deep Feedforward Neural Network and Shallow Architectures Effectiveness Comparison: Flight Delays Classification Perspective

A Deep Feedforward Neural Network and Shallow Architectures Effectiveness Comparison: Flight Delays Classification Perspective

The personalized recommendation for OTA flight cancellation and change services during the pandemic

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