# Imbalance Learning for Variable Star Classification

arXiv: https://arxiv.org/abs/2002.12386 · HTML: https://mlsanity.com/paper/2002.12386/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2020.

## Built on by (most cited first, 7 of 7)

- [Deep Attention-Based Supernovae Classification of Multi-Band Light-Curves](https://mlsanity.com/paper/2201.08482/) (2022)
- [Predicting machine failures from multivariate time series: an industrial case study](https://mlsanity.com/paper/2402.17804/) (2024)
- [MeerCRAB: MeerLICHT Classification of Real and Bogus Transients using Deep Learning](https://mlsanity.com/paper/2104.13950/) (2021)
- [Benchmark of Data Preprocessing Methods for Imbalanced Classification](https://mlsanity.com/paper/2303.03094/) (2023)
- [A Review of Machine Learning Techniques in Imbalanced Data and Future Trends](https://mlsanity.com/paper/2310.07917/) (2023)
- [Drifting Features: Detection and evaluation in the context of automatic RRLs identification in VVV](https://mlsanity.com/paper/2105.01714/) (2021)
- [Estimating Uncertainty in Galaxy Morphology Classification](https://mlsanity.com/paper/2608.08398/) (2026)

## Abstract (arXiv)

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: textitRandomly textitAugmented textitSampled textitLight curves from magnitude textitError (textttRASLE), augmenting light curves with Gaussian Process modelling (textttGpFit) and the Synthetic Minority Over-sampling Technique (textttSMOTE). 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 textttGpFit 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.