Fusing multi-granular-ball fuzzy information to detect outliers

Abstract

Outlier detection plays a critical role in data mining and machine learning, and its application value is widely recognized in several industries. However, despite the growing importance of outlier detection, many current outlier detection methods still rely on a single and fine-granularity processing paradigm. Not only does this paradigm lead to inefficient methods, but it also makes the methods vulnerable to noisy data. Furthermore, this processing paradigm ignores the potential multi-granularity information in the data, which may lead to an incomplete understanding of the intrinsic relations and patterns of the data. To further improve the performance of outlier detection, multi-granular-ball fuzzy information granules-based unsupervised outlier detection method (MGBOD) is proposed in this work. In our method, granular-balls with different granularity are first generated and the fuzzy binary relations between the granular-balls with respect to different attributes are computed. Subsequently, two attribute sequences are constructed based on the importance of the attributes. Then, multi-granular-ball fuzzy binary granular structures are constructed based on these two sequences. Finally, the outlier score of the granular-ball is defined by fusing these granules in the granular structures and mapped to the samples in the granular-ball. Experimental results show that, compared with recently proposed methods, our method demonstrates excellent outlier detection performance under a variety of public datasets. The code is publicly available at https://github.com/Mxeron/MGBOD.