Xinyu Su, Xiwen Wang, Dezhong Peng, Hongmei Chen, Yingke Chen, and Zhong Yuan*, Granular‑ball computing guided anomaly detection for hybrid attribute data, International Journal of Machine Learning and Cybernetics, 2024, DOI: 10.1007/s13042-024-02425-8.
Anomaly detection is one of the important research areas in data mining or data analytics. However, most of the existing anomaly detection methods struggle with heterogeneous data that includes both nominal and numerical attribute data. Moreover, these methods also suffer from inefficiency and noise sensitivity due to their single-granularity sample-based input paradigm. In this study, we propose an unsupervised anomaly detection method based on the granular-ball fuzzy set called HGBAD. First, we define a novel granular-ball fuzzy set to deal with the uncertainty information in hybrid attribute data. Based on the novel fuzzy set, multiple granular-ball fuzzy information granules are constructed. The anomaly degrees of granular-ball fuzzy information granules are fused to calculate the anomaly factors. The anomaly factors are used to measure the anomaly degrees of samples. Based on the anomaly factors, anomalies can be detected by an anomaly determination threshold. Experimental results demonstrate the superior performance of HGBAD in detecting anomalies across various data types. The code is publicly available at https://github.com/Mxeron/HGBAD.
You can run HGBAD.py:
if __name__ == '__main__':
data = pd.read_csv("./Example.csv").values
sigma = 0.6
out_factors = HGBAD(data, sigma)
print(out_factors)You can get outputs as follows:
out_factors = [0.37513644 0.40236464 0.34346751 0.39648027 0.36365423 0.34144782
0.38701767 0.38893099 0.38578651 0.34648678 0.35439573 0.37120788
0.35991082 0.33280241 0.3308039 0.35823686 0.34502298 0.36494364
0.36962932 0.35060029]
If you find HGBAD useful in your research, please consider citing:
@article{su2024granular,
title={Granular‑ball computing guided anomaly detection for hybrid attribute data},
author={Su, Xin Yu and Wang, Xi Wen and Peng, Deng Zhong and Chen, Hong Mei and Chen, Ying Ke and Yuan, Zhong},
journal={International Journal of Machine Learning and Cybernetics},
volume={},
number={},
pages={},
year={2024},
doi={10.1007/s13042-024-02425-8},
publisher={Springer}
}
If you have any questions, please contact [email protected] or [email protected].