Consistency-guided semi-supervised outlier detection in heterogeneous data using fuzzy rough sets

TL;DR

This paper introduces a semi-supervised outlier detection method for heterogeneous data using fuzzy rough sets, leveraging partial labels and consistency measures to improve detection accuracy.

Contribution

It proposes a novel consistency-guided outlier detection algorithm that effectively handles heterogeneous data with fuzzy rough set theory in a semi-supervised framework.

Findings

Outperforms or matches leading outlier detectors on 15 datasets.

Effectively utilizes partial labels and fuzzy similarity relations.

Demonstrates robustness across diverse heterogeneous data.

Abstract

Outlier detection aims to find samples that behave differently from the majority of the data. Semi-supervised detection methods can utilize the supervision of partial labels, thus reducing false positive rates. However, most of the current semi-supervised methods focus on numerical data and neglect the heterogeneity of data information. In this paper, we propose a consistency-guided outlier detection algorithm (COD) for heterogeneous data with the fuzzy rough set theory in a semi-supervised manner. First, a few labeled outliers are leveraged to construct label-informed fuzzy similarity relations. Next, the consistency of the fuzzy decision system is introduced to evaluate attributes' contributions to knowledge classification. Subsequently, we define the outlier factor based on the fuzzy similarity class and predict outliers by integrating the classification consistency and the outlier factor. The proposed algorithm is extensively evaluated on 15 freshly proposed datasets. Experimental results demonstrate that COD is better than or comparable with the leading outlier detectors. 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.asoc.2024.112070