Web-scale classification with naive bayes

Traditional Naive Bayes Classifier performs miserably on web-scale taxonomies. In this paper, we investigate the reasons behind such bad performance. We discover that the low performance are not completely caused by the intrinsic limitations of Naive Bayes, but mainly comes from two largely ignored problems: contradiction pair problem and discriminative evidence cancelation problem. We propose modifications that can alleviate the two problems while preserving the advantages of Naive Bayes. The experimental results show our modified Naive Bayes can significantly improve the performance on real web-scale taxonomies. Categories and Subject Descriptors: I.2.6 [Artificial Intelligence]: Learning General Terms: Algorithms, Experimentation

• Type: Conference
• Year: 2009
• Where: WWW
• Authors: Congle Zhang, Gui-Rong Xue, Yong Yu, Hongyuan Zha
• Keywords: Discriminative Evidence Cancelation, Internet Technology, Naive Bayes, Traditional Naive Bayes, WWW 2009