A novel attribute reduction algorithm based on peer-to-peer technique and rough set theory
Paper
Paper/Presentation Title | A novel attribute reduction algorithm based on peer-to-peer technique and rough set theory |
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Presentation Type | Paper |
Authors | Ma, Guangzhi (Author), Lu, Yansheng (Author), Wen, Peng (Author) and Song, Engmin (Author) |
Editors | Li, Yan, Yang, Jiajia, Wen, Peng and Wu, Jinglong |
Journal or Proceedings Title | Proceedings of the 2010 IEEE/ICME International Conference on Complex Medical Engineering (ICME 2010) |
Number of Pages | 5 |
Year | 2010 |
Place of Publication | Brisbane, Australia |
ISBN | 9781424468416 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ICCME.2010.5558832 |
Web Address (URL) of Paper | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5558832 |
Conference/Event | 2010 IEEE/ICME International Conference on Complex Medical Engineering (ICME 2010) |
Event Details | 2010 IEEE/ICME International Conference on Complex Medical Engineering (ICME 2010) Parent ICME International Conference on Complex Medical Engineering Event Date 13 to end of 15 Jul 2010 Event Location Gold Coast, Australia |
Abstract | 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. |
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 |
ANZSRC Field of Research 2020 | 460902. Decision support and group support systems |
461399. Theory of computation not elsewhere classified | |
490405. Group theory and generalisations | |
Public Notes | © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Byline Affiliations | Huazhong University of Science and Technology, China |
Department of Electrical, Electronic and Computer Engineering | |
Centre for Systems Biology | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q0z15/a-novel-attribute-reduction-algorithm-based-on-peer-to-peer-technique-and-rough-set-theory
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