利用标签信息提升异构数据异常检测精度。
Label-Informed Outlier Detection Based on Granule Density
- 基于模糊粒计算构建标签引导的粒度表示
- 仅需少量标注异常样本即实现高精度检测
- 适用于复杂多类型数据，适合实际场景应用
异常检测在众多应用中至关重要，但现有半监督方法常将数据视为纯数值且确定性处理，忽视真实数据中的异质性和不确定性。本文提出基于粒计算与模糊集的标签引导异常检测方法GDOF，首先通过标签引导的模糊粒化有效表示多种数据类型，并建立粒度密度以实现精确密度估计；随后通过评估属性相关性，结合少量已知异常样本整合各属性粒度密度进行异常评分。在多个真实数据集上的实验表明，该方法在仅使用少量标注异常样本时，对异构数据的异常检测表现优异。模糊集与粒计算的融合为复杂多样数据提供了实用的异常检测框架。所有数据集与源代码公开可查。本文为作者投稿至IEEE Transactions on Fuzzy Systems的接受稿，最终版本见https://doi.org/10.1109/TFUZZ.2024.3514853。
原文摘要 · Abstract (English)
Outlier detection, crucial for identifying unusual patterns with significant implications across numerous applications, has drawn considerable research interest. Existing semi-supervised methods typically treat data as purely numerical and} in a deterministic manner, thereby neglecting the heterogeneity and uncertainty inherent in complex, real-world datasets. This paper introduces a label-informed outlier detection method for heterogeneous data based on Granular Computing and Fuzzy Sets, namely Granule Density-based Outlier Factor (GDOF). Specifically, GDOF first employs label-informed fuzzy granulation to effectively represent various data types and develops granule density for precise density estimation. Subsequently, granule densities from individual attributes are integrated for outlier scoring by assessing attribute relevance with a limited number of labeled outliers. Experimental results on various real-world datasets show that GDOF stands out in detecting outliers in heterogeneous data with a minimal number of labeled outliers. The integration of Fuzzy Sets and Granular Computing in GDOF offers a practical framework for outlier detection in complex and diverse data types. All relevant datasets and source codes are publicly available for further research. This is the author's accepted manuscript of a paper published in IEEE Transactions on Fuzzy Systems. The final version is available at https://doi.org/10.1109/TFUZZ.2024.3514853
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