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. Classi2ers 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. The authors and the University of Edinburgh retain the right to reproduce and publish this paper for non-commercial purposes. Permission is granted for this report to be reproduced by others for non-commercial purposes as long as this copyright notice is reprinted in full in any reproduction. Applications to make other use of the material should be addressed in the first instance to Copyright Permissions, School of Informatics, The University of Edinburgh, 2 Buccleuch Place, Edinburgh EH8 9LW, Scotland.  ARTICLE IN PRESS 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.
Jensen and Shen REF proposed classical rough sets based on a dependency function of fuzzy-rough sets and presented the new greedy algorithm for reduction of redundant attributes.
Fuzzy rough attribute reduction with application to web categorization
APPEARS IN FUZZY SETS AND SYSTEMS
Computer Science Mathematics