Federated Meta-Learning for Fraudulent Credit Card Detection
Authors: Wenbo Zheng, Lan Yan, Chao Gou, Fei-Yue Wang
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that the proposed approach achieves significantly higher performance compared with the other state-of-the-art approaches. |
| Researcher Affiliation | Academia | Wenbo Zheng 1,2 , Lan Yan 2,4 , Chao Gou 3 and Fei-Yue Wang 2,4 1 School of Software Engineering, Xi an Jiaotong University 2 The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences 3 School of Intelligent Systems Engineering, Sun Yat-sen University 4 School of Artificial Intelligence, University of Chinese Academy of Sciences zwb2017@stu.xjtu.edu.cn, yanlan2017@ia.ac.cn, gouchao@mail.sysu.edu.cn, feiyue.wang@ia.ac.cn |
| Pseudocode | Yes | Algorithm 1: Federated Meta-Learning Approach |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | ECC: We sourced the first dataset from the European Credit Card (ECC) transactions provided by the ULB ML Group [Dal Pozzolo, 2015]. RA: We sourced the second dataset from the Revolution Analytics (RA)[Mohammed et al., 2018]; SD and Vesta: We sourced the third and fourth datasets from Kaggle; the third dataset is synthetic dataset (SD) 1 to evaluate the performance of fraud detection methods; the fourth dataset 2, is a challenging large-scale dataset, which comes from Vesta s real-world e-commerce transactions and contains a wide range of features from device type to product features. 1https://www.kaggle.com/ntnu-testimon/paysim1 2https://www.kaggle.com/c/ieee-fraud-detection/overview |
| Dataset Splits | No | The paper describes meta-learning specific 'support set' and 'training set' for its meta-training/testing phases, but it does not provide explicit train/validation/test dataset splits (e.g., percentages or counts) for the overall datasets used in the experiments (ECC, RA, SD, Vesta). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Adam optimizer' and 'Res Net-34 architecture' but does not specify any software names with version numbers for libraries, frameworks, or programming languages. |
| Experiment Setup | Yes | We employ the Res Net-34 architecture [He et al., 2016] for learning the feature exaction model. When meta-learn the transferable feature exaction, we use Adam optimizer [Kingma and Ba, 2014] with a learning rate of 0.001 and a decay for every 40 epochs. We totally train 1000 epochs and adopt the semi-hard mining strategy [Harwood et al., 2017] when the loss starts to converge. |