By Topic

A novel attribute reduction algorithm based on peer-to-peer technique and rough set theory

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Guangzhi Ma ; Sch. of Comput. Sci. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan, China ; Yansheng Lu ; Peng Wen ; Engmin Song

Rough Set theory is an effective tool to deal with vagueness and uncertainty information to select the most relevant attributes for a decision system. However, to find the minimum attributes is a NP-hard problem. In this paper, we describe a method to decrease the scale of the problem by filtering core attributes, and then employ the checking tree to test the rest attributes from bottom to top by using peer-to-peer technique. Furthermore, we utilize pruning method to enhance the speed and discard the node when one of its child node superset of certain attribute reduction found before. Experimental results show that our parallel algorithm has the high speed-up ratio while the attribute reductions are distributed in the bottom of the tree. In a peer-to-peer network, our algorithm will amortize the required memory on client computers. Accordingly, this algorithm can be applied to deal with larger data set in a distributed environment.

Published in:

Complex Medical Engineering (CME), 2010 IEEE/ICME International Conference on

Date of Conference:

13-15 July 2010