.png){kind=icon}
返回
Fuzzy-rough attribute reduction with application to web categorization
.png){kind=icon}
OA
AI
DOI：10.1016/S0165-0114(03)00021-6.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Due to the explosive growth of electronically stored information, automatic methods must be developed to aid users in maintaining and using this abundance of information effectively. In particular, the sheer volume of redundancy present must be dealt with, leaving only the information-rich data to be processed. This paper presents a novel approach, based on an integrated use of fuzzy and rough set theories, to greatly reduce this data redundancy. Formal concepts of fuzzy-rough attribute reduction are introduced and illustrated with a simple example. The work is applied to the problem of web categorization, considerably reducing dimensionality with minimal loss of information. Experimental results show that fuzzy-rough reduction is more powerful than the conventional rough set-based approach. Classifiers that use a lower dimensional set of attributes which are retained by fuzzy-rough reduction outperform those that employ more attributes returned by the existing crisp rough reduction method. (C) 2003 Elsevier B.V. All rights reserved.
Keyword:
attribute reduction
web categorization
data redundancy
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:
2.7
论文数:
7.6K
被引数:
1.5W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏
查看更多内容