Outlier detection in mixed-attribute data: a semi-supervised approach with fuzzy approximations and relative entropy

TL;DR

This paper presents FROD, a semi-supervised outlier detection method for mixed-attribute data that uses fuzzy rough sets, attribute classification accuracy, and fuzzy relative entropy to improve detection performance.

Contribution

It introduces a novel semi-supervised outlier detection approach combining fuzzy approximations and relative entropy, effectively handling uncertainty and heterogeneity in mixed-attribute data.

Findings

FROD performs comparably or better than existing algorithms on 16 datasets.

The method effectively leverages limited labeled data for improved detection.

Experimental results validate the robustness of the proposed approach.

Abstract

Outlier detection is a critical task in data mining, aimed at identifying objects that significantly deviate from the norm. Semi-supervised methods improve detection performance by leveraging partially labeled data but typically overlook the uncertainty and heterogeneity of real-world mixed-attribute data. This paper introduces a semi-supervised outlier detection method, namely fuzzy rough sets-based outlier detection (FROD), to effectively handle these challenges. Specifically, we first utilize a small subset of labeled data to construct fuzzy decision systems, through which we introduce the attribute classification accuracy based on fuzzy approximations to evaluate the contribution of attribute sets in outlier detection. Unlabeled data is then used to compute fuzzy relative entropy, which provides a characterization of outliers from the perspective of uncertainty. Finally, we develop the detection algorithm by combining attribute classification accuracy with fuzzy relative entropy. Experimental results on 16 public datasets show that FROD is comparable with or better than leading detection algorithms. All datasets and source codes are accessible at https://github.com/ChenBaiyang/FROD. This manuscript is the accepted author version of a paper published by Elsevier. The final published version is available at https://doi.org/10.1016/j.ijar.2025.109373