Abstract
The number of bags mishandled while transferring to a connecting flight is high. Bags at-risk of missing their connections can be processed faster; however, identifying such bags at-risk is still done by simple business rules. This work researches a general model of baggage transfer process and proposes a Gradient Boosting Machine based prediction model for identifying the bags at-risk. Our prediction model is compared to the current rule based method and a benchmark using logistic regression. The results show that our model offers an increase in accuracy coupled with a marked increase in precision and recall when identifying bags that are transferred unsuccessfully.
Original language | English |
---|---|
Title of host publication | ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence |
Editors | Ana Rocha, Luc Steels, Jaap van den Herik |
Publisher | SCITEPRESS-Science and Technology Publications, Lda. |
Pages | 172-181 |
Number of pages | 10 |
ISBN (Electronic) | 9789897583957 |
DOIs | |
Publication status | Published - Feb 2020 |
Event | 12th International Conference on Agents and Artificial Intelligence, ICAART 2020 - Valletta, Malta Duration: 22 Feb 2020 → 24 Feb 2020 |
Conference
Conference | 12th International Conference on Agents and Artificial Intelligence, ICAART 2020 |
---|---|
Country | Malta |
City | Valletta |
Period | 22/02/20 → 24/02/20 |
Keywords
- Baggage At-risk Prediction
- Baggage Transfer Process Model
- Gradient Boosting Machine