Consistency-guided semi-supervised outlier detection in heterogeneous data using fuzzy rough sets
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
Code (0)
등록된 구현이 없습니다.
Tasks
Outlier DetectionSimilar Papers 제목 키워드 기반
OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers
Semi-supervised learning (SSL) is an effective means to leverage unlabeled data to improve a model's performance. Typical SSL methods like FixMatch assume that labeled and unlabeled data share the same label space. Howev…
Novelty DetectionOutlier DetectionOpenMatch: Open-Set Semi-supervised Learning with Open-set Consistency Regularization
Semi-supervised learning (SSL) is an effective means to leverage unlabeled data to improve a model’s performance. Typical SSL methods like FixMatch assume that labeled and unlabeled data share the same label space. Howev…
Novelty DetectionOutlier DetectionSemi-Supervised Image ClassificationALRe: Outlier Detection for Guided Refinement
Guided refinement is a popular procedure of various image post-processing applications. It produces output image based on input and guided images. Input images are usually flawed estimates containing kinds of noises and …
Outlier DetectionSemi-supervised Contrastive Outlier removal for Pseudo Expectation Maximization (SCOPE)
Semi-supervised learning is the problem of training an accurate predictive model by combining a small labeled dataset with a presumably much larger unlabeled dataset. Many methods for semi-supervised deep learning have b…
Contrastive LearningOutlier detection in mixed-attribute data: a semi-supervised approach with fuzzy approximations and relative entropy
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 t…
Outlier Detection