SAFE

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

(★ 35)

No description available.

SAFE Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# 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/)

SAFE

SAFE Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

SAFE

SAFE Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README