Galaxy Zoo Express is a framework which provides a solution for a practical combination of human and machine classifiers for the task of galaxy morphology. GZX utilizes a slightly modified version of SWAP (Marshall et al. 2016) to increase human classification efficiency interwoven with a random forest supervised machine learning algorithm. Together, this system achieves an order of magnitude increase in the rate of galaxy classification when applied to the Galaxy Zoo 2 classification database.

A publication is in prep and expected to be submitted by December 2016.