Hosenie, Z, Lyon, R, Stappers, B, Mootoovaloo, A and McBride, V (2020) Imbalance learning for variable star classification. Monthly Notices of the Royal Astronomical Society, 493 (4). pp. 6050-6059. ISSN 0035-8711

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Abstract

The accurate automated classification of variable stars into their respective subtypes is difficult. Machine learning–based solutions often fall foul of the imbalanced learning problem, which causes poor generalization performance in practice, especially on rare variable star subtypes. In previous work, we attempted to overcome such deficiencies via the development of a hierarchical machine learning classifier. This ‘algorithm-level’ approach to tackling imbalance yielded promising results on Catalina Real-Time Survey (CRTS) data, outperforming the binary and multiclass classification schemes previously applied in this area. In this work, we attempt to further improve hierarchical classification performance by applying ‘data-level’ approaches to directly augment the training data so that they better describe underrepresented classes. We apply and report results for three data augmentation methods in particular: Randomly Augmented Sampled Light curves from magnitude Error (RASLE), augmenting light curves with Gaussian Process modelling (GpFit) and the Synthetic Minority Oversampling Technique (SMOTE). When combining the ‘algorithm-level’ (i.e. the hierarchical scheme) together with the ‘data-level’ approach, we further improve variable star classification accuracy by 1–4 per cent. We found that a higher classification rate is obtained when using GpFit in the hierarchical model. Further improvement of the metric scores requires a better standard set of correctly identified variable stars, and perhaps enhanced features are needed.

Item Type:	Article
Additional Information:	This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.
Uncontrolled Keywords:	0201 Astronomical and Space Sciences; Astronomy & Astrophysics
Subjects:	Q Science > QB Astronomy
Divisions:	Computer Science & Mathematics
Publisher:	Oxford University Press (OUP)
SWORD Depositor:	A Symplectic
Date Deposited:	15 Sep 2023 14:29
Last Modified:	15 Sep 2023 14:31
DOI or ID number:	10.1093/mnras/staa642
URI:	https://researchonline.ljmu.ac.uk/id/eprint/21460

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