Anomaly Detection with Autoencoder and Random Forest

Tzu Hsuan Lin, Jehn Ruey Jiang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

This paper proposes AERFAD, an anomaly detection method based on the autoencoder and the random forest, for solving the credit card fraud detection problem. The proposed AERFAD first utilizes the autoencoder to reduce the dimensionality of data and then uses the random forest to classify data as anomalous or normal. Large numbers of credit card transaction data of European cardholders are applied to AEFRAD to detect possible frauds for the sake of performance evaluation. When compared with related methods, AERFAD has relatively excellent performance in terms of the accuracy, true positive rate, true negative rate, and Matthews correlation coefficient.

原文???core.languages.en_GB???
主出版物標題Proceedings - 2020 International Computer Symposium, ICS 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面96-99
頁數4
ISBN(電子)9781728192550
DOIs
出版狀態已出版 - 12月 2020
事件2020 International Computer Symposium, ICS 2020 - Tainan, Taiwan
持續時間: 17 12月 202019 12月 2020

出版系列

名字Proceedings - 2020 International Computer Symposium, ICS 2020

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???event.eventtypes.event.conference???2020 International Computer Symposium, ICS 2020
國家/地區Taiwan
城市Tainan
期間17/12/2019/12/20

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