miscellaneous-9

Classification Of Variable Stars

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Imbalance Learning for Variable Star Classification

R. Lyon, Zafiirah Hosenie, Benjamin W. Stappers, A. Mootoovaloo, Vanessa McBride•Wed Feb 26 2020

This work attempts to further improve hierarchical classification performance by applying ‘data-level’ approaches to directly augment the training data so that they better describe underrepresented classes, and finds that a higher classification rate is obtained when using GpFit in the hierarchical model.

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Streaming Classification of Variable Stars

Lukas Zorich, K. Pichara, P. Protopapas•Tue Dec 03 2019

A streaming probabilistic classification model that uses a set of newly designed features that work incrementally to achieve high classification performance, staying an order of magnitude faster than traditional classification approaches.

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Scalable End-to-end Recurrent Neural Network for Variable star classification

K. Pichara, P. Protopapas, I. Becker, M. Catelán, Carlos Aguirre, Fatemeh Nikzat•Sun Feb 02 2020

This work proposes an end-to-end algorithm that automatically learns the representation of light curves that allows an accurate automatic classification, and uses minimal data pre-processing, can be updated with a low computational cost for new observations and light curves, and can scale up to massive data sets.

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