Imbalance Learning for Variable Star Classification

• URL: http://arxiv.org/abs/2002.12386v1
• Date: Thu, 27 Feb 2020 19:01:05 GMT
• Title: Imbalance Learning for Variable Star Classification
• Authors: Zafiirah Hosenie, Robert Lyon, Benjamin Stappers, Arrykrishna Mootoovaloo and Vanessa McBride
• Abstract summary: We develop a hierarchical machine learning classification scheme to overcome imbalanced learning problems. We use 'data-level' approaches to directly augment the training data so that they better describe under-represented classes. We find that a higher classification rate is obtained when using $texttGpFit$ in the hierarchical model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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