Summary of Bias Correction in Machine Learning-based Classification Of Rare Events, by Luuk Gubbels et al.
Bias Correction in Machine Learning-based Classification of Rare Events
by Luuk Gubbels, Marco Puts, Piet Daas
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 machine learning-based approach for identifying online platform businesses is introduced, combining natural language processing and rare event detection techniques. The task of accurately classifying web-scraped texts into online platforms is challenging due to their rarity. To address this issue, a text classification algorithm is developed that minimizes false positives by leveraging calibrated probabilities and ensembles. This approach reduces bias in estimates and has the potential to revolutionize the field of online platform detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Online businesses on the internet can be hard to find because they are rare. Scientists have created a new way to use machine learning to identify these businesses using texts from the web. They combined two important areas: natural language processing and detecting rare events. This makes it easier to find online platforms without getting many false results. |
Keywords
» Artificial intelligence » Event detection » Machine learning » Natural language processing » Text classification
