Ensemble Models for Detecting Wikidata Vandalism with Stacking - Team Honeyberry Vandalism Detector at WSDM Cup 2017
Yamazaki Tomoya Yahoo Japan Corporation, Sasaki Mei Yahoo Japan Corporation, Murakami Naoya Yahoo Japan Corporation, Makabe Takuya Yahoo Japan Corporation, Iwasawa Hiroki Yahoo Japan Corporation
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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.