Fuzzy–rough attribute reduction with application to web categorization

✍ Scribed by Richard Jensen; Qiang Shen

• Publisher: Elsevier Science
• Year: 2004
• Tongue: English
• Weight: 384 KB
• Volume: 141
• Category: Article
• ISSN: 0165-0114
• DOI: 10.1016/s0165-0114(03)00021-6

✦ Synopsis

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 e ectively. 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. Classiÿers 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.