Summary of Automated Machine Learning For Positive-unlabelled Learning, by Jack D. Saunders and Alex A. Freitas
Automated Machine Learning for Positive-Unlabelled Learning
by Jack D. Saunders, Alex A. Freitas
First submitted to arxiv on: 12 Jan 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 This paper proposes two new Automated Machine Learning (Auto-ML) systems for Positive-Unlabelled (PU) learning. PU learning involves training classifiers on data with labelled positive instances and unlabelled instances that can be either positive or negative. The Auto-ML systems, BO-Auto-PU and EBO-Auto-PU, use Bayesian Optimisation and evolutionary/Bayesian optimisation approaches respectively to select optimal models for PU learning tasks. The paper also compares these new Auto-ML systems with established PU learning methods on 60 datasets (20 real-world datasets with varying PU characteristics). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using machines to learn from data that has some information, but not all of it. It’s like trying to figure out what a cat likes best without asking the cat directly! The researchers created new tools to help with this problem and tested them on lots of different datasets. They want to make it easier for people to use these tools and find the right one for their specific problem. |
Keywords
* Artificial intelligence * Machine learning
