标题： 基于模糊粒密度的多尺度粒球异常检测 显示英文标题

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

摘要： 异常检测是指识别与正常数据分布显著偏离的异常样本，并已在各种实际任务中得到了广泛研究和应用。 然而，大多数无监督异常检测方法是专门设计来检测指定的异常值，而现实数据可能混杂着不同类型的异常值。 在本研究中，我们提出了一种基于模糊粗糙集的多尺度异常检测方法，以识别各种类型的异常值。 具体而言，首先引入了一种基于模糊粗糙集的新方法，该方法结合了相对模糊粒度密度，以提高检测局部异常值的能力。 然后，提出了一种基于粒球计算的多尺度视图生成方法，以协作方式在不同粒度层次上识别群体异常值。 此外，通过三元决策确定的可靠异常值和正常值用于训练加权支持向量机，以进一步提高异常检测的性能。 所提出的方法创新性地将无监督异常检测转化为半监督分类问题，并首次从多尺度粒球的角度探索基于模糊粗糙集的异常检测，使其能够高度适应不同类型的异常值。 在人工和UCI数据集上进行的大量实验表明，所提出的异常检测方法显著优于最先进的方法，在接收者操作特征曲线下的面积（AUROC）指标上至少提高了8.48%。 {源代码在\url{https://github.com/Xiaofeng-Tan/MGBOD}发布。} 显示英文摘要

摘要： 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}. }