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

TL;DR

This paper introduces a novel multi-scale, fuzzy rough sets-based outlier detection method that effectively identifies various outlier types by integrating granular-ball computing and semi-supervised learning, outperforming existing techniques.

Contribution

It proposes a new fuzzy rough sets-based multi-scale outlier detection approach using granular-ball computing and semi-supervised SVM training, enhancing adaptability and detection accuracy.

Findings

Significantly outperforms state-of-the-art methods in AUROC by at least 8.48%.

Effectively detects local and group outliers across different datasets.

Transforms unsupervised outlier detection into a semi-supervised classification task.

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}. }