research-article

Fusing multi-scale fuzzy information to detect outliers

Authors:

Zhong YuanAuthors Info & Claims

Volume 103, Issue C

https://doi.org/10.1016/j.inffus.2023.102133

Published: 04 March 2024 Publication History

Abstract

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.

Highlights

•

A novel information fusion model based on multi-scale fuzzy granules is formulated.

•

An unsupervised outlier detection algorithm that integrates multi-scale information is proposed.

•

Extensive experiments indicate the effectiveness and advantages of our method over some state-of-the-arts.

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Index Terms

Fusing multi-scale fuzzy information to detect outliers
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Probabilistic reasoning
      2. Vagueness and fuzzy logic
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Information Fusion

Information Fusion Volume 103, Issue C

Mar 2024

918 pages

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Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 04 March 2024

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