Predicting the Costs of Forwarding Contracts Using XGBoost and a Deep Neural Network

• Autorzy: Podlodowski Łukasz , Kozłowski Marek

Identyfikatory

DOI

10.15439/2022F295

• Konferencja: Federated Conference on Computer Science and Information Systems (17 ; 04-07.09.2022 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

This article presents an application of an XGBoostand deep neural network ensemble as a solution for a task assigned at the FedCSIS 2022 Challenge: Predicting the Costs of Forwarding Contracts. We demonstrate that prediction quality can be improved by combining the two approaches. We present a neural network architecture based on three independent flows. We then discuss the influence of long short-term memory units on the risk of overfitting. Finally, we show that the static XGBoost model can complement a neural network that processes dynamic data.

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 30
• Strony: 425--429
• Opis fizyczny: Bibliogr. 16 poz.

Twórcy

autor

Podlodowski Łukasz

autor

Kozłowski Marek