Random forest automated supervised classification of Hipparcos periodic variable stars

@article{Dubath2011RandomFA,
  title={Random forest automated supervised classification of Hipparcos periodic variable stars},
  author={Pierre Dubath and Lorenzo Rimoldini and Maria Suveges and J. Blomme and M. L'opez and Luis M. Sarro and Joris De Ridder and Johan Peter Cuypers and Leanne P. Guy and I. Lecoeur and Krzysztof Nienartowicz and A. Jan and M. Beck and Nami Mowlavi and Peter De Cat and Thomas Lebzelter and Laurent Eyer},
  journal={Monthly Notices of the Royal Astronomical Society},
  year={2011},
  volume={414},
  pages={2602-2617}
}
We present an evaluation of the performance of an automated classification of the Hipparcos periodic variable stars into 26 types. The sub-sample with the most reliable variability types available in the literature is used to train supervised algorithms to characterize the type dependencies on a number of attributes. The most useful attributes evaluated with the random forest methodology include, in decreasing order of importance, the period, the amplitude, the V − I colour index, the absolute… 
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References

SHOWING 1-10 OF 36 REFERENCES
A study of supervised classification of Hipparcos variable stars using PCA and Support Vector Machines
We report on the automated classification of Hipparcos variable stars by a supervised classification algorithm known as Support Vector Machines. The dataset comprised about 3200 stars, each
Automated classification of variable stars for All‐Sky Automated Survey 1–2 data
With the advent of surveys generating multi-epoch photometry and the discovery of large numbers of variable stars, the classification of these stars has to be automatic. We have developed such a
Automated supervised classification of variable stars - I. Methodology
TLDR
An overview of the stellar variability classes that are presently known, in terms of some relevant stellar parameters, to use the class descriptions obtained as the basis for an automated ``supervised classification'' of large databases.
Comparative clustering analysis of variable stars in the Hipparcos, OGLE Large Magellanic Cloud, and CoRoT exoplanet databases
Context. Discovery of new variability classes in large surveys using multivariate statistics techniques such as clustering, relies heavily on the correct understanding of the distribution of known
Automated supervised classification of variable stars in the CoRoT programme. Method and application
Context: Aims: In this work, we describe the pipeline for the fast supervised classification of light curves observed by the CoRoT exoplanet CCDs. We present the classification results obtained for
Automated Classification of Variable Stars in the Asteroseismology Program of the Kepler Space Mission
We present the first results of the application of supervised classification methods to the Kepler Q1 long-cadence light curves of a subsample of 2288 stars measured in the asteroseismology program
Light-curve classification in massive variability surveys - II. Transients towards the Large Magellanic Cloud
Automatic classification of variability is now possible with tools such as neural networks. Here, we present two neural networks for the identification of microlensing events: the first discriminates
Lightcurve Classification in Massive Variability Surveys
This paper pioneers the use of neural networks to provide a fast and automatic way to classify lightcurves in massive photometric datasets. As an example, we provide a working neural network that can
Light-curve classification in massive variability surveys — I. Microlensing
This paper exploits neural networks to provide a fast and automatic way to classify light curves in massive photometric data sets. As an example, we provide a working neural network that can
Random Forests
TLDR
Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
...
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