Ensemble-based Method of Fraud Detection at Self-checkouts in Retail

• Autorzy: Vitynskyi P. , Tkachenko R. , Izonin I.
• Języki publikacji: EN

Abstrakty

EN

The authors consider the problem of fraud detection at self-checkouts in retail in condition of unbalanced data set. A new ensemble-based method is proposed for its effective solution. The developed method involves two main steps: application of the preprocessing procedures and the Random Forest algorithm. The step-by-step implementation of the preprocessing stage involves the sequential execution of such procedures over the input data: scaling by maximal element in a column with row-wise scaling by Euclidean norm, weighting by correlation and applying polynomial extension. For polynomial extension Ito decomposition of the second degree is used. The simulation of the method was carried out on real data. Evaluating performance was based on the use of cost matrix. The experimental comparison of the effectiveness of the developed ensemble-based method with a number of existing (simples and ensembles) demonstrates the best performance of the developed method. Experimental studies of changing the parameters of the Random Forest both for the basic algorithm and for the developed method demonstrate a significant improvement of the investigated efficiency measures of the latter. It is the result of all steps of the preprocessing stage of the developed method use.

Słowa kluczowe

EN

classification; Ensemble-based method; Random Forest; fraud detection; retail; Ito decomposition; imbalanced dataset

• Wydawca: Polish Academy of Sciences, Branch in Lublin
• Czasopismo: ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
• Rocznik: 2019
• Tom: Vol. 8, No 2
• Strony: 3--8
• Opis fizyczny: Bibliogr. 14 poz., rys., wykr., wz.

Twórcy

autor

Vitynskyi P.

• Department of Publishing Information Technologies, Lviv Polytechnic National University, S. Bandery 28a, 79008 Lviv, Ukraine

autor

Tkachenko R.

• Department of Publishing Information Technologies, Lviv Polytechnic National University, S. Bandery 28a, 79008 Lviv, Ukraine

autor

Izonin I.

• Department of Publishing Information Technologies, Lviv Polytechnic National University, S. Bandery 28a, 79008 Lviv, Ukraine

Bibliografia

• 1. Molnár E., Molnár R., Kryvinska N., Greguš M. 2014. Web Intelligence in practice. The Society of Service Science, Journal of Service Science Research, Springer, Vol. 6, No. 1: 149-172.
• 2. BEETLE / iSCAN EASY SCO. URL: https://www.dieboldnixdorf.com/en-us/retail/systems/self-checkout-solutions/beetle-iscan-easy-sco (last accessed 10.06.2019)
• 3. Kryvinska N. 2012. Building Consistent Formal Specification for the Service Enterprise Agility Foundation. The Society of Service Science, Journal of Service Science Research, Springer, Vol. 4, No. 2: 235-269.
• 4. Kaczor S., Kryvinska N. 2013. It is all about Services - Fundamentals, Drivers, and Business Models. The Society of Service Science, Journal of Service Science Research, Springer, Vol. 5, No. 2, 2013: 125-154.
• 5. Gregus M., Kryvinska N. 2015. Service Orientation of Enterprises - Aspects, Dimensions, Technologies. Comenius University in Bratislava, ISBN: 9788022339780.
• 6. Kryvinska N., Gregus M. 2014. SOA and it's Business Value in Requirements, Features, Practices and Methodologies. Comenius University in Bratislava, ISBN: 9788022337649.
• 7. Wang S. 2010. A Comprehensive Survey of Data Mining-Based Accounting-Fraud Detection Research. International Conference on Intelligent Computation Technology and Automation, Changsha: 50-53.
• 8. Izonin I. et. All 2018. The Combined Use of the Wiener Polynomial and SVM for Material Classification Task in Medical Implants Production. International Journal of Intelligent Systems and Applications, Vol.10, No. 9: .40-47.
• 9. Tepla T. L., et all. 2018. Alloys selection based on the supervised learning technique for design of biocompatible medical materials. Archives of Materials Science and Engineering, vol. 1, no. 93: 32–40.
• 10. Tepla T., Izonin I., Duriagina Z. 2019. Biocompatible materials selection via new supervised learning methods. LAP Lambert Academic Publishing, Riga, Latvia, 114 p.
• 11. Vitynskyi P. et al. 2018. Hybridization of the SGTM Neural-like Structure through Inputs Polynomial Extension. In: Proceedings of the 2018 IEEE Second International Conference on Data Stream Mining & Processing, 21–25 August 2018, Lviv, Ukraine, 2018: 386-391.
• 12. DATA MINING CUP 2019. URL: https://www.data-mining-cup.com/dmc-2019/ (last accessed 10.06.2019).
• 13. Gruszczyński K. 2019. Enhancing business process event logs with software failure data. Econtechmod. Vol 8, no 1: 27-32.
• 14. Anokhin M., Koryttsev I. 2015. Decision-making Rule Estimation with Applying similarity Metrics. Econtechmod. Vol 4, no 3: 73-78.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).