Baiyang Chen, Yongxiang Li, Dezhong Peng, Hongmei Chen, and Zhong Yuan*, "Fusing multi-scale fuzzy information to detect outliers," (PDF) Information Fusion, vol. 103, p. 102133, 8 November 2023, DOI: 10.1016/j.inffus.2023.102133
Outlier detection aims to find objects that behave differently from the majority of the data. Existing unsupervised approaches often process data with a single scale, which may not capture the multi-scale nature of the data. In this paper, we propose a novel information fusion model based on multi-scale fuzzy granules and an unsupervised outlier detection algorithm with the fuzzy rough set theory. First, a multi-scale information fusion model is formulated based on fuzzy granules. Then we employ fuzzy approximations to define the outlier factor of multi-scale fuzzy granules centered at each data point. Finally, the outlier score is calculated by aggregating the outlier factors of a set of multi-scale fuzzy granules.
- python=3.8
- numpy=1.23
- scikit-learn=1.2
To reproduce the results in paper:
python run_reproduce.py
To reproduce the examples in paper:
python run_example.py
To run MFIOD on customised datastes:
To be updated later.
If you find the code or datasets useful in your research, please consider citing:
@article{Chen2024MFIOD,
title = {Fusing multi-scale fuzzy information to detect outliers},
author = {Baiyang Chen and Yongxiang Li and Dezhong Peng and Hongmei Chen and Zhong Yuan},
journal = {Information Fusion},
volume = {103},
pages = {102133},
year = {2024},
issn = {1566-2535},
doi = {https://doi.org/10.1016/j.inffus.2023.102133},
url = {https://www.sciencedirect.com/science/article/pii/S1566253523004499}
}
or:
Baiyang Chen, Yongxiang Li, Dezhong Peng, Hongmei Chen, and Zhong Yuan, "Fusing multi-scale fuzzy information to detect outliers," Information Fusion, vol. 103, p. 102133, doi: 10.1016/j.inffus.2023.102133
If you have any questions, please contact farstars@qq.com or yuanzhong@scu.edu.cn.