https://github.com/ILoveAI2019/OCAN

OCAN: One-Class Adversarial Nets for Fraud Detection
https://github.com/ILoveAI2019/OCAN

Last synced: 25 days ago
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OCAN: One-Class Adversarial Nets for Fraud Detection

• Host: GitHub
• URL: https://github.com/ILoveAI2019/OCAN
• Owner: ILoveAI2019
• Created: 2018-09-01T23:25:19.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-09-12T21:54:28.000Z (about 6 years ago)
• Last Synced: 2024-08-03T18:16:18.572Z (4 months ago)
• Language: Python
• Size: 16 MB
• Stars: 22
• Watchers: 1
• Forks: 10
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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• awesome-fraud-detection-papers - [Code

README

        

# OCAN: One-Class Adversarial Nets for Fraud Detection

In this paper, we develop one-class adversarial nets (OCAN) for fraud detection with only benign users as training data. 

## Running Environment

The main packages you need to install are listed as follow

```

1. python 2.7 

2. tensorflow 1.3.0

```

## DateSet

For experiments, we evaluate **OCAN** on two real-world datasets: wiki and credit-card which have been attached in folder [data/](https://github.com/PanpanZheng/OCAN/tree/master/data).

## Model Evaluation

The command line for OCAN goes as follow

```

    python oc_gan.py $1 $2

```

**where** $1 refers to different datasets with wiki 1, credit-card(encoding) 2 and credit-card(raw) 3; $2 denotes whether some metrics, such as fm_loss and f1 in training process, are provided, with non-display 0 and display 1.

```

   e.g. python oc_gan.py 1 0 

```

The above command line shows the performance of OCAN on wiki without displaying metrics in the training process.