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arXiv · 1712.06921

Ensemble Models for Detecting Wikidata Vandalism with Stacking - Team Honeyberry Vandalism Detector at WSDM Cup 2017

Tomoya Yamazaki·Mei Sasaki·Naoya Murakami·Takuya Makabe·Hiroki Iwasawa
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Abstract

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.

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Tomoya Yamazaki, Mei Sasaki, Naoya Murakami, Takuya Makabe, Hiroki Iwasawa. 2017-12-19. Ensemble Models for Detecting Wikidata Vandalism with Stacking - Team Honeyberry Vandalism Detector at WSDM Cup 2017. https://arxiv.org/abs/1712.06921

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