Machine Learning Based on Resampling Approaches and Deep Reinforcement Learning for Credit Card Fraud Detection Systems
| Autor: | Mai Viet Tiep, Luc Minh Tuan, Tran Khanh Dang, Thanh Cong Tran |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Technology
Computer science QH301-705.5 QC1-999 Machine learning computer.software_genre resampling techniques Imbalanced data Resampling Reinforcement learning General Materials Science Biology (General) Instrumentation QD1-999 Fluid Flow and Transfer Processes credit card fraud deep reinforcement learning business.industry Process Chemistry and Technology Physics Credit card fraud General Engineering Engineering (General). Civil engineering (General) Computer Science Applications Chemistry machine learning classification imbalanced data Artificial intelligence TA1-2040 business F1 score computer |
| Zdroj: | Applied Sciences Volume 11 Issue 21 Applied Sciences, Vol 11, Iss 10004, p 10004 (2021) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app112110004 |
| Popis: | The problem of imbalanced datasets is a significant concern when creating reliable credit card fraud (CCF) detection systems. In this work, we study and evaluate recent advances in machine learning (ML) algorithms and deep reinforcement learning (DRL) used for CCF detection systems, including fraud and non-fraud labels. Based on two resampling approaches, SMOTE and ADASYN are used to resample the imbalanced CCF dataset. ML algorithms are, then, applied to this balanced dataset to establish CCF detection systems. Next, DRL is employed to create detection systems based on the imbalanced CCF dataset. The diverse classification metrics are indicated to thoroughly evaluate the performance of these ML and DRL models. Through empirical experiments, we identify the reliable degree of ML models based on two resampling approaches and DRL models for CCF detection. When SMOTE and ADASYN are used to resampling original CCF datasets before training/test split, the ML models show very high outcomes of above 99% accuracy. However, when these techniques are employed to resample for only the training CCF datasets, these ML models show lower results, particularly in terms of logistic regression with 1.81% precision and 3.55% F1 score for using ADASYN. Our work reveals the DRL model is ineffective and achieves low performance, with only 34.8% accuracy. |
| Databáze: | OpenAIRE |
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