Efficient Generation of Hidden Outliers for Improved Outlier Detection
Outlier generation is a popular technique used for solving important outlier detection tasks. Generating outliers with realistic behavior is challenging. Popular existing methods tend to disregard the 'multiple views' property of outliers in high-dimensional spaces. The only existing method accounting for this property falls short in efficiency and effectiveness. We propose BISECT, a new outlier generation method that creates realistic outliers mimicking said property. To do so, BISECT employs a novel proposition introduced in this article stating how to efficiently generate said realistic outliers. Our method has better guarantees and complexity than the current methodology for recreating 'multiple views'. We use the synthetic outliers generated by BISECT to effectively enhance outlier detection in diverse datasets, for multiple use cases. For instance, oversampling with BISECT reduced the error by up to 3 times when compared with the baselines.
Code (1)
Tasks
Outlier DetectionSimilar Papers 제목 키워드 기반
Outlier Detection Using a Novel method: Quantum Clustering
We propose a new assumption in outlier detection: Normal data instances are commonly located in the area that there is hardly any fluctuation on data density, while outliers are often appeared in the area that there is v…
ClusteringOutlier DetectionRobust Subspace Outlier Detection in High Dimensional Space
Rare data in a large-scale database are called outliers that reveal significant information in the real world. The subspace-based outlier detection is regarded as a feasible approach in very high dimensional space. Howev…
Outlier DetectionVocal Bursts Intensity PredictionDiffusion based Semantic Outlier Generation via Nuisance Awareness for Out-of-Distribution Detection
Out-of-distribution (OOD) detection, which determines whether a given sample is part of the in-distribution (ID), has recently shown promising results through training with synthetic OOD datasets. Nonetheless, existing m…
Out-of-Distribution DetectionOut of Distribution (OOD) DetectionFinding Inner Outliers in High Dimensional Space
Outlier detection in a large-scale database is a significant and complex issue in knowledge discovering field. As the data distributions are obscure and uncertain in high dimensional space, most existing solutions try to…
Outlier DetectionVocal Bursts Intensity PredictionGenerating Artificial Outliers in the Absence of Genuine Ones -- a Survey
By definition, outliers are rarely observed in reality, making them difficult to detect or analyse. Artificial outliers approximate such genuine outliers and can, for instance, help with the detection of genuine outliers…
BenchmarkingExperimental DesignOutlier Detection