# Mitigating shortage of labeled data using clustering-based active learning with diversity exploration

arXiv: https://arxiv.org/abs/2207.02964 · HTML: https://mlsanity.com/paper/2207.02964/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 15 references that are in the map.
- First posted on arXiv 6 Jul 2022.

## Built on by (most cited first, 1 of 1)

- [Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection](https://mlsanity.com/paper/2306.00765/) (2023)

## Abstract (arXiv)

In this paper, we proposed a new clustering-based active learning framework, namely Active Learning using a Clustering-based Sampling (ALCS), to address the shortage of labeled data. ALCS employs a density-based clustering approach to explore the cluster structure from the data without requiring exhaustive parameter tuning. A bi-cluster boundary-based sample query procedure is introduced to improve the learning performance for classifying highly overlapped classes. Additionally, we developed an effective diversity exploration strategy to address the redundancy among queried samples. Our experimental results justified the efficacy of the ALCS approach.