Summary of Efficient Generation Of Hidden Outliers For Improved Outlier Detection, by Jose Cribeiro-ramallo et al.
Efficient Generation of Hidden Outliers for Improved Outlier Detection
by Jose Cribeiro-Ramallo, Vadim Arzamasov, Klemens Böhm
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel outlier generation method, BISECT, is proposed to create realistic outliers that mimic the “multiple views” property in high-dimensional spaces. Existing methods disregard this property or lack efficiency and effectiveness. BISECT employs a new proposition to efficiently generate realistic outliers with better guarantees and complexity than current methodology. This method has been used to effectively enhance outlier detection in diverse datasets, reducing error rates by up to 3 times compared to baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make fake data that looks like real outliers is developed. Outliers are things that don’t fit the normal pattern of data. The existing methods for making these fake outliers don’t take into account how they might look from different angles, which is important in high-dimensional spaces. This new method, called BISECT, uses a special idea to make realistic outliers that have this “multiple views” property. By using BISECT, researchers can make their data better by reducing errors and improving outlier detection. |
Keywords
* Artificial intelligence * Outlier detection
