Summary of Anchoral: Computationally Efficient Active Learning For Large and Imbalanced Datasets, by Pietro Lesci and Andreas Vlachos
AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets
by Pietro Lesci, Andreas Vlachos
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 The proposed AnchorAL method addresses the challenges of standard pool-based active learning for imbalanced classification tasks. By selecting class-specific instances as anchors, retrieving similar unlabelled instances, and using a fixed-sized subpool, AnchorAL promotes class balance, prevents overfitting, and discovers new minority instance clusters. This approach outperforms competing methods in terms of speed, model performance, and dataset balance across various classification tasks, strategies, and architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AnchorAL is a new way to make machine learning models better for imbalanced data problems. Right now, it’s hard to find the rare instances that are important for these problems because they don’t show up often. To solve this, we need to look at lots of unlabelled data. The usual way to do this takes too long and doesn’t work well. AnchorAL is a better approach. It picks out special instances called anchors from the labelled data and finds similar ones in the unlabelled data. Then it uses those similar instances to help the model learn. This makes the process faster, more accurate, and helps find new minority instances. |
Keywords
* Artificial intelligence * Active learning * Classification * Machine learning * Overfitting
