Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

9-2023

Abstract

Given a limited labeling budget, active learning (al) aims to sample the most informative instances from an unlabeled pool to acquire labels for subsequent model training. To achieve this, al typically measures the informativeness of unlabeled instances based on uncertainty and diversity. However, it does not consider erroneous instances with their neighborhood error density, which have great potential to improve the model performance. To address this limitation, we propose Real, a novel approach to select data instances with Representative Errors for Active Learning. It identifies minority predictions as pseudo errors within a cluster and allocates an adaptive sampling budget for the cluster based on estimated error density. Extensive experiments on five text classification datasets demonstrate that Real consistently outperforms all best-performing baselines regarding accuracy and F1-macro scores across a wide range of hyperparameter settings. Our analysis also shows that Real selects the most representative pseudo errors that match the distribution of ground-truth errors along the decision boundary. Our code is publicly available at https://github.com/withchencheng/ECML_PKDD_23_Real.

Keywords

Active Learning, Error density, Error-driven, Informativeness, Labelings, Model training, Neighbourhood, Pseudo errors, Text classification

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Turin, Italy, 2023 September 18-22

First Page

20

Last Page

37

ISBN

9783031434112

Identifier

10.1007/978-3-031-43412-9_2

Publisher

Springer

City or Country

New York

Additional URL

https://doi.org/10.1007/978-3-031-43412-9_2

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