Label-Informed Outlier Detection Based on Granule Density

TL;DR

This paper presents GDOF, a novel label-informed outlier detection method that leverages Granular Computing and Fuzzy Sets to effectively identify outliers in heterogeneous data with limited labeled examples.

Contribution

It introduces GDOF, a new outlier detection approach combining fuzzy granulation and density estimation tailored for complex, diverse datasets with minimal supervision.

Findings

GDOF outperforms existing methods on real-world datasets.

Effective detection of outliers with few labeled examples.

Framework applicable to various data types.

Abstract

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