Credit Card Fraud Detection Using Autoencoder Neural Network

2019/08/30 by Junyi Zou, Zou, Junyi, Jinliang Zhang +3 · 1 citation
Computer Science · Engineering · #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Rough Sets and Fuzzy Logic #Vehicle License Plate Recognition

paper · pdf · doi:10.48550/arxiv.1908.11553

openalex publication_date 2019/08/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Imbalanced data classification problem has always been a popular topic in the field of machine learning research. In order to balance the samples between majority and minority class. Oversampling algorithm is used to synthesize new minority class samples, but it could bring in noise. Pointing to the noise problems, this paper proposed a denoising autoencoder neural network (DAE) algorithm which can not only oversample minority class sample through misclassification cost, but it can denoise and classify the sampled dataset. Through experiments, compared with the denoising autoencoder neural network (DAE) with oversampling process and traditional fully connected neural networks, the results showed the proposed algorithm improves the classification accuracy of minority class of imbalanced datasets.

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• Data Leakage and Deceptive Performance: A Critical Examination of Credit Card Fraud Detection Methodologies

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