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Proceedings of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 30

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

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DOI: http://dx.doi.org/10.15439/2022F295

Citation: Proceedings of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 30, pages 425–429 ()

Full text

Abstract. 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.

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