# Cold Start Active Learning Strategies in the Context of Imbalanced Classification

arXiv: https://arxiv.org/abs/2201.10227 · HTML: https://mlsanity.com/paper/2201.10227/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 25 Jan 2022.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Low-shot learning with large-scale diffusion](https://mlsanity.com/paper/1706.02332/) (2017)
- [Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale](https://mlsanity.com/paper/1605.05797/) (2016)
- [Tweet2Vec: Character-Based Distributed Representations for Social Media](https://mlsanity.com/paper/1605.03481/) (2016)

## Built on by (most cited first, 2 of 2)

- [DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning](https://mlsanity.com/paper/2502.00305/) (2025)
- [STENCIL: Submodular Mutual Information Based Weak Supervision for Cold-Start Active Learning](https://mlsanity.com/paper/2402.13468/) (2024)

## Abstract (arXiv)

We present novel active learning strategies dedicated to providing a solution to the cold start stage, i.e. initializing the classification of a large set of data with no attached labels. Moreover, proposed strategies are designed to handle an imbalanced context in which random selection is highly inefficient. Specifically, our active learning iterations address label scarcity and imbalance using element scores, combining information extracted from a clustering structure to a label propagation model. The strategy is illustrated by a case study on annotating Twitter content w.r.t. testimonies of a real flood event. We show that our method effectively copes with class imbalance, by boosting the recall of samples from the minority class.