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https://github.com/PanpanZheng/SAFE
https://github.com/PanpanZheng/SAFE
Last synced: 25 days ago
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- Host: GitHub
- URL: https://github.com/PanpanZheng/SAFE
- Owner: PanpanZheng
- Created: 2018-06-19T19:54:51.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-05-24T20:30:10.000Z (over 4 years ago)
- Last Synced: 2024-08-03T18:16:17.360Z (4 months ago)
- Language: Python
- Size: 42.7 MB
- Stars: 33
- Watchers: 2
- Forks: 10
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-fraud-detection-papers - [Code
README
# SAFE: A Neural Survival Analysis Model for Fraud Early Detection
In this paper, we propose a survival analysis based fraud early detection model, SAFE, that maps dynamic user activities to survival probabilities that are guaranteed to be monotonically decreasing along time.
## Running Environment
The main packages you need to install
```
1. python 2.7
2. tensorflow 1.3.0
```
## DateSet
For experiments, we evaluate **SAFE** on two real-world datasets: twitter and wiki which have been attached in [twitter/](https://github.com/PanpanZheng/SAFE/tree/master/twitter) and [wiki/](https://github.com/PanpanZheng/SAFE/tree/master/wiki), respectively.
## Model Evaluation
The command lines for SAFE and baselines go as follow
* **SAFE**
```
python framework/safe.py $1
```
* **M-LSTM**
```
python framework/base_rnn.py $1
```
* **CPH & SVM**
```
python framework/safe_baselines.py $1
```
**where** *$1* refers to datasets on which the model runs, and it can be assigned as 'twitter' or 'wiki'.
* **Weibull & other distributions**
```
python framework/safe_distr.py $1 $2
```
**where** *$1* refers to the corresponding distributions and it can be assigned as 'exp' (exponential), 'ray' (Rayleigh) and 'poi' (poisson); *$2* denotes the datasets, 'twitter' or 'wiki'.
## Authors
* **Panpan Zheng, Shuhan Yuan and Xintao Wu**
- [personal website](https://sites.uark.edu/pzheng/)
- [google scholar](https://scholar.google.com/citations?user=f2OLKMYAAAAJ&hl=en)
## Citation
I am very glad that you could visit this github and check my research work. If it benefits your work, please cite the paper in Arxiv https://arxiv.org/abs/1809.04683v1
.
## Acknowledgments
This work was going on underlying the guide of prof. [Xintao Wu](http://csce.uark.edu/~xintaowu/) and Dr. [Shuhan Yuan](https://sites.uark.edu/sy005/).
Appreciate it greatly for every labmate in [**SAIL lab**](https://sail.uark.edu/)