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.
The main packages you need to install
1. python 2.7
2. tensorflow 1.3.0
For experiments, we evaluate SAFE on two real-world datasets: twitter and wiki which have been attached in twitter/ and wiki/, respectively.
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'.
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Panpan Zheng, Shuhan Yuan and Xintao Wu
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 .
This work was going on underlying the guide of prof. Xintao Wu and Dr. Shuhan Yuan.
Appreciate it greatly for every labmate in SAIL lab