@inproceedings{f73052ea454a412eb5bb44002b31893f,
title = "Anomaly Detection with Autoencoder and Random Forest",
abstract = "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. ",
keywords = "anomaly detection, autoencoder, deep learning, random forest",
author = "Lin, {Tzu Hsuan} and Jiang, {Jehn Ruey}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Computer Symposium, ICS 2020 ; Conference date: 17-12-2020 Through 19-12-2020",
year = "2020",
month = dec,
doi = "10.1109/ICS51289.2020.00028",
language = "???core.languages.en_GB???",
series = "Proceedings - 2020 International Computer Symposium, ICS 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "96--99",
booktitle = "Proceedings - 2020 International Computer Symposium, ICS 2020",
}