Efficient Generation of Hidden Outliers for Improved Outlier Detection
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916376400101376 |
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| author | Cribeiro-Ramallo, Jose Arzamasov, Vadim Böhm, Klemens |
| author_facet | Cribeiro-Ramallo, Jose Arzamasov, Vadim Böhm, Klemens |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_03846 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Generation of Hidden Outliers for Improved Outlier Detection Cribeiro-Ramallo, Jose Arzamasov, Vadim Böhm, Klemens Machine Learning 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. |
| title | Efficient Generation of Hidden Outliers for Improved Outlier Detection |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.03846 |