Random forest classifiers classify Kepler targets and select analysis methods to measure surface rotation periods of solar-like stars.
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2 Pith papers cite this work. Polarity classification is still indexing.
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astro-ph.SR 2years
2019 2verdicts
UNVERDICTED 2representative citing papers
Random Forest model classifies K2 stars into four categories with over 80% accuracy using effective temperature, luminosity, and FliPer features.
citing papers explorer
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Determining surface rotation periods of solar-like stars observed by the Kepler mission using machine learning techniques
Random forest classifiers classify Kepler targets and select analysis methods to measure surface rotation periods of solar-like stars.
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Automatic classification of K2 pulsating stars using machine learning techniques
Random Forest model classifies K2 stars into four categories with over 80% accuracy using effective temperature, luminosity, and FliPer features.