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

2025-01-06

IEEE Transactions on Knowledge and Data Engineering

AI Summary

Existing anomaly detection methods struggle to uniformly model local anomalies, collective anomalies, and their complex hybrid patterns. This paper proposes an unsupervised anomaly detection framework that synergistically integrates fuzzy rough set theory with multi-scale granular ball computing—the first such integration. It introduces a relative fuzzy granular density measure and transforms anomaly detection into a weighted SVM-driven semi-supervised classification problem via multi-granularity collaborative modeling and a three-way decision mechanism. Key contributions are: (1) a multi-scale granular ball-guided fuzzy rough granulation method; and (2) a three-way decision-guided semi-supervised optimization paradigm. Evaluated on synthetic and UCI benchmark datasets, the method achieves an average AUROC improvement of ≥8.48% over state-of-the-art approaches. The source code is publicly available.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Multi-instance/Multi-view Learning
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

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. { The source codes are released at url{https://github.com/Xiaofeng-Tan/MGBOD}. }

Problem

• Anomaly Detection
• Complex Data
• Local and Collective Anomalies

Innovation

• Fuzzy Rough Set Theory
• Granular Ball Concept
• Semi-Supervised Learning for Anomaly Detection