This item is a Poster.
- Zhang, Congle - Shanghai Jiao Tong University
- Xue, Gui-Rong - Shanghai Jiao Tong University
- Yu, Yong - Shanghai Jiao Tong University
- Zha, Hongyuan - Georgia Institute of Technology
Published Version
![[img]](../style/images/fileicons/application_pdf.png)
| PDF (795Kb) |
Abstract
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.
Export Record As...
- HTML Citation
- ASCII Citation
- Resource Map
- OpenURL ContextObject
- EndNote
- BibTeX
- OpenURL ContextObject in Span
- MODS
- DIDL
- EP3 XML
- JSON
- Dublin Core
- Reference Manager
- Eprints Application Profile
- Simple Metadata
- Refer
- METS
