Mitigating shortage of labeled data using clustering-based active learning with diversity exploration
Abstract
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.
Cite
@article{arxiv.2207.02964,
title = {Mitigating shortage of labeled data using clustering-based active learning with diversity exploration},
author = {Xuyang Yan and Shabnam Nazmi and Biniam Gebru and Mohd Anwar and Abdollah Homaifar and Mrinmoy Sarkar and Kishor Datta Gupta},
journal= {arXiv preprint arXiv:2207.02964},
year = {2022}
}
Comments
Accepted by the ICML 2022 Workshop on Adaptive Experimental Design and Active Learning in the Real World