چکیده مقاله
Losses related to fraudulent transactions areincreasing, so building a fraud detection system is essential Previous studies have employed a variety of data mining andmachine learning techniques to construct fraud detectionsystems This study presents a new hybrid method based on thesupervised autoencoder and the extreme gradient boosting XGBoost method This combined method uses the power of asupervised autoencoder to generate an expressive representationof the data It employs the XGBoost method as a robust classifierto detect fraudulent transactions The hyperparameters of theproposed method are fine tuned using the Bayesian optimizationalgorithm The experiments on a public dataset containing 280thousand records demonstrated that the proposed methodachieves better results than the baseline method considering allthe performance criteria, including Recall, Precision, and F1measure
کلیدواژهها
نویسندگان
Hossein Abbasimehr
Assistant ProfessorFaculty of Information Technologyand Computer EngineeringAzarbaijan Shahid MadaniUniversityTabriz, Iran
Hosein Fanai
Faculty of Information Technologyand Computer EngineeringAzarbaijan Shahid MadaniUniversityTabriz, Iran
شیوه ارجاع
Abbasimehr, Hossein and Fanai, Hosein,1401,A combined approach of the supervised autoencoderand XGBoost method for credit card fraud detection,1st International Conference and 6th National Conference on Computers, information technology and applications of artificial intelligence
ارائهشده در
مجموعه مقالات اولین کنفرانس بین المللی و ششمین کنفرانس ملی کامپیوتر، فناوری اطلاعات و کاربردهای هوش مصنوعی3 اسفند 1401 · اهواز