Anomaly Detection with Autoencoder and Random Forest

Tzu Hsuan Lin, Jehn Ruey Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings - 2020 International Computer Symposium, ICS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages96-99
Number of pages4
ISBN (Electronic)9781728192550
DOIs
StatePublished - Dec 2020
Event2020 International Computer Symposium, ICS 2020 - Tainan, Taiwan
Duration: 17 Dec 202019 Dec 2020

Publication series

NameProceedings - 2020 International Computer Symposium, ICS 2020

Conference

Conference2020 International Computer Symposium, ICS 2020
Country/TerritoryTaiwan
CityTainan
Period17/12/2019/12/20

Keywords

  • anomaly detection
  • autoencoder
  • deep learning
  • random forest

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