.png){kind=icon}
返回
Combining subclassifiers in text categorization: A DST-based solution and a case study
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2007.190663.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Text categorization systems often use machine learning techniques to induce document classifiers from preclassified examples. The fact that each example document belongs to many classes often leads to very high computational costs that sometimes grow exponentially in the number of features. Seeking to reduce these costs, we explored the possibility of running a baseline induction algorithm separately for subsets of features, obtaining a set of classifiers to be combined. For the specific case of classifiers that return not only class labels but also confidences in these labels, we investigate here a few alternative fusion techniques, including our own mechanism that was inspired by the Dempster-Shafer Theory. The paper describes the algorithm and, in our specific case study, compares its performance to that of more traditional mechanisms.
Keyword:
machine learning
text categorization
multilabel examples
fusion
Dempster-Shafer Theory
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
机构
暂无机构信息
引用论文
Structures of SRP54 and SRP19, the Two Proteins that Organize the Ribonucleic Core of the Signal Recognition Particle from Pyrococcus furiosus.png)
PLoS ONE
OAAI
PLoS ONEIF0
OAAI分享收藏分享收藏
查看更多内容