Logo PTI Logo FedCSIS

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

Annals of Computer Science and Information Systems, Volume 30

Prediction of the Costs of Forwarding Contracts with Machine Learning Methods

DOI: http://dx.doi.org/10.15439/2022F298

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 413416 ()

Full text

Abstract. This paper summarizes conducted experiments andfindings related to FedCSIS 2022 Challenge that we participated in. The task was to develop a predictive model that estimated costs pertained to the execution of forwarding contracts (FC). We thoroughly analyze the dataset and present steps performed in the data preprocessing stage. Then we describe our approach to building a predictive model, which placed us eighth out of 135 teams. In the end, a wide range of ideas for further research is provided.

References

  1. N. Limao and A. J. Venables, “Infrastructure, geographical disadvantage, transport costs, and trade,” The world bank economic review, vol. 15, no. 3, pp. 451–479, 2001.
  2. A. Micco and N. Pérez, “Determinants of maritime transport costs,” Inter-American Development Bank, 2002.
  3. G. Wilmsmeier, J. Hoffmann, and R. J. Sanchez, “The impact of port characteristics on international maritime transport costs,” Research in transportation economics, vol. 16, pp. 117–140, 2006.
  4. Y. Konishi, S.-i. Mun, Y. Nishiyama, and J. E. Sung, Determinants of Transport Costs for Inter-regional Trade. Research Inst. of Economy, Trade and Industry, 2012.
  5. H.-s. Cho, “Determinants and effects of logistics costs in container ports: The transaction cost economics perspective,” The Asian Journal of Shipping and Logistics, vol. 30, no. 2, pp. 193–215, 2014.
  6. S. Camisón-Haba and J. A. Clemente, “A global model for the estimation of transport costs,” Economic research-Ekonomska istraživanja, vol. 33, no. 1, pp. 2075–2100, 2020.
  7. A. Janusz, A. Jamiołkowski, and M. Okulewicz, “Predicting the costs of forwarding contracts: Analysis of data mining competition results,” in Proceedings of the 17th Conference on Computer Science and Intelligence Systems, FedCSIS 2022, Sofia, Bulgaria, September 4-7, 2022. IEEE, 2022.
  8. S. García, J. Luengo, and F. Herrera, Data preprocessing in data mining. Springer, 2015, vol. 72.
  9. A. Jain, “Complete Guide to Parameter Tuning in XGBoost with codes in Python,” 2016, online; accessed 23-Jul-2022. [Online]. Available: https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/
  10. D. Martins, “XGBoost: A Complete Guide to Fine-Tune and Optimize your Model,” 2021, online; accessed 23-Jul-2022. [Online]. Available: https://towardsdatascience.com/xgboost-fine-tune-and-optimize-your-model-23d996fab663
  11. J. Lu, A. Liu, F. Dong, F. Gu, J. Gama, and G. Zhang, “Learning under concept drift: A review,” IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 12, pp. 2346–2363, 2018.