{"paper":{"title":"Ensemble Models for Detecting Wikidata Vandalism with Stacking - Team Honeyberry Vandalism Detector at WSDM Cup 2017","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Hiroki Iwasawa (Yahoo Japan Corporation), Mei Sasaki, Naoya Murakami, Takuya Makabe, Tomoya Yamazaki","submitted_at":"2017-12-19T13:39:52Z","abstract_excerpt":"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."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.06921","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}