Fuzzy Granule Density-Based Outlier Detection With Multi-Scale Granular Balls

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorGao, Can
dc.contributor.authorTan, Xiaofeng
dc.contributor.authorZhou, Jie
dc.contributor.authorDing, Weiping
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2025-04-18T10:07:02Z
dc.date.available2025-04-18T10:07:02Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractOutlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. © 1989-2012 IEEE.
dc.description.sponsorshipThis work was supported in part by the National Natural Science Foundation of China under Grant 62476171, Grant 62476172, Grant 62076164, and Grant U2433216, in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2024A1515011367, in part by Natural Science Foundation of Jiangsu Province under Grant BK20231337, in part by Guangdong Provincial Key Laboratory under Grant 2023B1212060076, and in part by Shenzhen Institute of Artificial Intelligence and Robotics for Society. The authors would like to thank the editor-in-chief, editor, and anonymous reviewers for their insightful and constructive comments to greatly improve the quality of the paper.
dc.identifier.citationGao, C., Tan, X., Zhou, J., Ding, W., & Pedrycz, W. (2025). Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls. IEEE Transactions on Knowledge and Data Engineering.
dc.identifier.doi10.1109/TKDE.2024.3525003
dc.identifier.endpage1197
dc.identifier.issn10414347
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85215847223
dc.identifier.scopusqualityQ1
dc.identifier.startpage1182
dc.identifier.urihttp://dx.doi.org/10.1109/TKDE.2024.3525003
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6947
dc.identifier.volume37
dc.identifier.wosWOS:001410873400019
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherIEEE Computer Society
dc.relation.ispartofIEEE Transactions on Knowledge and Data Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectFuzzy Granule Density
dc.subjectFuzzy Rough Sets
dc.subjectMulti-scale Granular Balls
dc.subjectOutlier Detection
dc.subjectThree-way Decision
dc.titleFuzzy Granule Density-Based Outlier Detection With Multi-Scale Granular Balls
dc.typeArticle

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