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

• URL: http://arxiv.org/abs/2207.02964v1
• Date: Wed, 6 Jul 2022 20:53:28 GMT
• Title: Mitigating shortage of labeled data using clustering-based active learning with diversity exploration
• Authors: Xuyang Yan, Shabnam Nazmi, Biniam Gebru, Mohd Anwar, Abdollah Homaifar, Mrinmoy Sarkar, and Kishor Datta Gupta
• Abstract summary: We propose a clustering-based active learning framework, namely Active Learning using a Clustering-based Sampling. A bi-cluster boundary-based sample query procedure is introduced to improve the learning performance for classifying highly overlapped classes.
• Score: 3.312798619476657
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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