The XGBoost Approach Tuned by TLB Metaheuristics for Fraud Detection

Aleksandar Petrovic; Milos Antonijevic; Ivana Strumberger; Luka Jovanovic; Nikola Savanovic; Stefana Janicijevic

doi:10.2991/978-94-6463-110-4_16

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The XGBoost Approach Tuned by TLB Metaheuristics for Fraud Detection

Authors

Aleksandar Petrovic¹, Milos Antonijevic¹, Ivana Strumberger¹^{, *}, Luka Jovanovic¹, Nikola Savanovic¹, Stefana Janicijevic¹

¹Singidunum University, Danijelova 32, 11000, Belgrade, Serbia

^*Corresponding author. Email: istrumberger@singidunum.ac.rs

Corresponding Author

Ivana Strumberger

Available Online 30 January 2023.

DOI: 10.2991/978-94-6463-110-4_16 How to use a DOI?
Keywords: fraud detection; swarm intelligence; metaheuristics; optimization; teaching-learning-based-optimization lgorithm
Abstract: The recent pandemic had a major impact on online transactions. With this trend, credit card fraud increased. For the solution to this problem the authors explore existing solutions and propose an optimized solution. The solution is based on an extreme gradient boosting algorithm (XGBoost) and a teaching-learning-based-optimization algorithm. The dataset optimizes the hyperparameters of the XGBoost which is utilized as the main driver for the solution. The evaluation was performed among other similar techniques that have solved this problem successfully in the past. Standard performance metrics were applied which are accuracy, recall, precision, Matthews correlation coefficient, and area under the curve. The result of this research presents a dominant solution that was proposed and successfully outperformed all other compared solutions overall.
Copyright: © 2023 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)
Series: Advances in Computer Science Research
Publication Date: 30 January 2023
ISBN: 10.2991/978-94-6463-110-4_16
ISSN: 2352-538X
DOI: 10.2991/978-94-6463-110-4_16 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Aleksandar Petrovic
AU  - Milos Antonijevic
AU  - Ivana Strumberger
AU  - Luka Jovanovic
AU  - Nikola Savanovic
AU  - Stefana Janicijevic
PY  - 2023
DA  - 2023/01/30
TI  - The XGBoost Approach Tuned by TLB Metaheuristics for Fraud Detection
BT  - Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)
PB  - Atlantis Press
SP  - 219
EP  - 234
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-110-4_16
DO  - 10.2991/978-94-6463-110-4_16
ID  - Petrovic2023
ER  -

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