  Quantifying galaxy morphology is a challenging yet scientifically rewarding
task. As the scale of data continues to increase with upcoming surveys,
traditional classification methods will struggle to handle the load. We present
a solution through an integration of visual and automated classifications,
preserving the best features of both human and machine. We demonstrate the
effectiveness of such a system through a re-analysis of visual galaxy
morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We
reprocess the top level question of the GZ2 decision tree with a Bayesian
classification aggregation algorithm dubbed SWAP, originally developed for the
Space Warps gravitational lens project. Through a simple binary classification
scheme we increase the classification rate nearly 5-fold, classifying 226,124
galaxies in 92 days of GZ2 project time while reproducing labels derived from
GZ2 classification data with 95.7% accuracy.
  We next combine this with a Random Forest machine learning algorithm that
learns on a suite of nonparametric morphology indicators widely used for
automated morphologies. We develop a decision engine that delegates tasks
between human and machine, and demonstrate that the combined system provides at
least a factor of 8 increase in the classification rate, classifying 210,803
galaxies in just 32 days of GZ2 project time with 93.1% accuracy. As the Random
Forest algorithm requires a minimal amount of computation cost, this result has
important implications for galaxy morphology identification tasks in the era of
Euclid and other large scale surveys.
