{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/imbalance-learning-for-variable-star","title":"Imbalance Learning for Variable Star Classification","arxiv_id":"2002.12386","date":"2020-02-27","proceeding":null,"authors":["Zafiirah Hosenie","Robert Lyon","Benjamin Stappers","Arrykrishna Mootoovaloo","Vanessa McBride"],"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.","url_abs":"https://arxiv.org/abs/2002.12386v1","url_pdf":"https://arxiv.org/pdf/2002.12386v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"imbalance-learning-for-variable-star","repo_url":"https://github.com/Zafiirah13/ICVaS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"imbalance-learning-for-variable-star","repo_url":"https://github.com/Zafiirah13/Imbalance-Learning-for-Variable-Star-Classification-using-Machine-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification-of-variable-stars","task_name":"Classification Of Variable Stars"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multi-class-classification","task_name":"Multi-class Classification"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2002.12386","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.12386"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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