计算机科学
质心
支持向量机
班级(哲学)
质量(理念)
投票
特征(语言学)
多数决原则
人工智能
数据挖掘
机器学习
情报检索
语言学
哲学
法学
认识论
政治
政治学
作者
Edgardo Ferretti,Leticia Cagnina,Viviana Paiz,Sebastián Delle Donne,Rodrigo Zacagnini,Marcelo Luis Errecalde
标识
DOI:10.1016/j.ipm.2018.08.003
摘要
In this work, we present the first quality flaw prediction study for articles containing the two most frequent verifiability flaws in Spanish Wikipedia: articles which do not cite any references or sources at all (denominated Unreferenced) and articles that need additional citations for verification (so-called Refimprove). Based on the underlying characteristics of each flaw, different state-of-the-art approaches were evaluated. For articles not citing any references, a well-established rule-based approach was evaluated and interesting findings show that some of them suffer from Refimprove flaw instead. Likewise, for articles that need additional citations for verification, the well-known PU learning and one-class classification approaches were evaluated. Besides, new methods were compared and a new feature was also proposed to model this latter flaw. The results showed that new methods such as under-bagged decision trees with sum or majority voting rules, biased-SVM, and centroid-based balanced SVM, perform best in comparison with the ones previously published.
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