Ma, Guangzhi and Lu, Yansheng and Wen, Peng and Song, Engmin (2010) A novel attribute reduction algorithm based on peer-to-peer technique and rough set theory. In: 2010 IEEE/ICME International Conference on Complex Medical Engineering (CME 2010), 13-15 July 2010, Gold Coast, Australia.
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.
|Item Type:||Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)|
|Additional Information:||© 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Uncontrolled Keywords:||attribute reduction algorithm; checking tree; child node superset; decision system; peer-to-peer network; peer-to-peer technique; pruning method; rough set theory; speed-up ratio|
|Depositing User:||epEditor USQ|
|Date Deposited:||06 Nov 2011 12:09|
|Last Modified:||03 Jul 2013 00:52|
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