Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
SAFE: A Neural Survival Analysis Model for Fraud Early Detection
Authors: Panpan Zheng, Shuhan Yuan, Xintao Wu1278-1285
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two real world datasets demonstrate that SAFE outperforms both the survival analysis model and recurrent neural network model alone as well as state-of-the-art fraud early detection approaches. |
| Researcher Affiliation | Academia | Panpan Zheng, Shuhan Yuan, Xintao Wu University of Arkansas, Fayetteville, AR, USA EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Repeatability. Our software together with the datasets are available at https://github.com/Panpan Zheng/SAFE. |
| Open Datasets | Yes | We conduct our experiments on two real-world datasets: Twitter... We adopt the UMDWikipedia dataset (Kumar, Spezzano, and Subrahmanian 2015)... Our software together with the datasets are available at https://github.com/Panpan Zheng/SAFE. |
| Dataset Splits | Yes | We randomly divide the dataset into a training set, a validation set, and a testing set with the ratio (7:1:2). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments. |
| Software Dependencies | No | The paper mentions using Adam for optimization and Lifelines for CPH implementation, but no specific version numbers for any software dependencies are provided. |
| Experiment Setup | Yes | SAFE is trained by back-propagation via Adam (Kingma and Ba 2015) with a batch size of 16 and a learning rate 10 3. The dimension of the GRU hidden unit is 32. |