Fusing multi-scale fuzzy information to detect outliers

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. Experimental results demonstrate that the proposed method is comparable with or better than the leading outlier detection methods. The codes and datasets are publicly available online at https://github.com/ChenBaiyang/MFIOD.