Anomaly Detection with Autoencoder and Random Forest
Tzu-Hsuan Lin, Jehn‐Ruey Jiang
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.
Topics & Concepts
AutoencoderRandom forestAnomaly detectionComputer scienceCredit cardCurse of dimensionalityAnomaly (physics)Database transactionArtificial intelligenceData miningPattern recognition (psychology)Correlation coefficientCredit card fraudMachine learningDeep learningDatabaseCondensed matter physicsPaymentPhysicsWorld Wide WebAnomaly Detection Techniques and ApplicationsImbalanced Data Classification TechniquesNetwork Security and Intrusion Detection