Imbalance Learning for Variable Star Classification

The accurate automated classification of variable stars into their respective sub-types is difficult. Machine learning based solutions often fall foul of the imbalanced learning problem, which causes poor generalisation performance in practice, especially on rare variable star sub-types. In previous...

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Bibliographic Details
Published in:arXiv.org 2020-02
Main Authors: Hosenie, Zafiirah, Lyon, Robert, Stappers, Benjamin, Mootoovaloo, Arrykrishna, McBride, Vanessa
Format: Article
Language:English
Subjects:
Algorithms
Classification
Gaussian process
Light curve
Machine learning
Variable stars
Online Access:Get full text
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Summary:The accurate automated classification of variable stars into their respective sub-types is difficult. Machine learning based solutions often fall foul of the imbalanced learning problem, which causes poor generalisation performance in practice, especially on rare variable star sub-types. 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 multi-class 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 under-represented classes. We apply and report results for three data augmentation methods in particular: \(\textit{R}\)andomly \(\textit{A}\)ugmented \(\textit{S}\)ampled \(\textit{L}\)ight curves from magnitude \(\textit{E}\)rror (\(\texttt{RASLE}\)), augmenting light curves with Gaussian Process modelling (\(\texttt{GpFit}\)) and the Synthetic Minority Over-sampling Technique (\(\texttt{SMOTE}\)). 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\(\%\). We found that a higher classification rate is obtained when using \(\texttt{GpFit}\) 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.
ISSN:2331-8422
DOI:10.48550/arxiv.2002.12386