Fuzzy Granule Density-Based Outlier Detection With Multi-Scale Granular Balls
Küçük Resim Yok
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE Computer Society
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Outlier 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.
Açıklama
Anahtar Kelimeler
Fuzzy Granule Density, Fuzzy Rough Sets, Multi-scale Granular Balls, Outlier Detection, Three-way Decision
Kaynak
IEEE Transactions on Knowledge and Data Engineering
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
37
Sayı
3
Künye
Gao, 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.
