  The WSDM Cup 2017 is a binary classification task for classifying Wikidata
revisions into vandalism and non-vandalism. This paper describes our method
using some machine learning techniques such as under-sampling, feature
selection, stacking and ensembles of models. We confirm the validity of each
technique by calculating AUC-ROC of models using such techniques and not using
them. Additionally, we analyze the results and gain useful insights into
improving models for the vandalism detection task. The AUC-ROC of our final
submission after the deadline resulted in 0.94412.
