Attribute reduction based on granular computing

Authors:
Jun Hu;GuoYin Wang;QingHua Zhang;XianQuan Liu
Affiliations:
School of Electronic Engineering, XiDian University, Xi’an, Shaanxi, P.R. China;School of Electronic Engineering, XiDian University, Xi’an, Shaanxi, P.R. China;Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, P.R. China;Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, P.R. China
Venue:
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Year:
2006

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Abstract

Attribute reduction is a very important issue in data mining and machine learning. Granular computing is a new kind of soft computing theory. A novel method for encoding granules using bitmap technique is proposed in this paper. A new attribute reduction method based on granular computing is also developed with this encoding method. It is proved to be efficient.