The Feasibility and Flexibility of Selecting Quasars by Variability
  Using Ensemble Machine Learning Algorithms
  In this work we train three decision-tree based ensemble machine learning
algorithms (Random Forest Classifier, Adaptive Boosting and Gradient Boosting
Decision Tree respectively) to study quasar selection in the variable source
catalog in SDSS Stripe 82. We build training and test samples (both containing
1:1 of quasars and stars) using the spectroscopic confirmed sources in SDSS
DR14 (including 8330 quasars and 3966 stars). We find that, trained with
variation parameters alone, all three models can select quasars with similarly
and remarkably high precision and completeness ($\sim$ 98.5% and 97.5%), even
better than trained with SDSS colors alone ($\sim$ 97.2% and 96.5%), consistent
with previous studies. Through applying the trained models on the variable
sources without spectroscopic identifications, we estimate the
spectroscopically confirmed quasar sample in Stripe 82 variable source catalog
is $\sim$ 93% complete (95% for $m_i<19.0$). Using the Random Forest Classifier
we derive the relative importance of the observational features utilized for
classifications. We further show that even using one- or two-year time domain
observations, variability-based quasar selection could still be highly
efficient.
