Title: Attribute Equilibrium Dominance Reduction Accelerator (DCCAEDR) Based on Distributed Coevolutionary Cloud and Its Application in Medical Records
Authors: Ding, Wei-Ping
Lin, Chin-Teng
Prasad, Mukesh
Chen, Sen-Bo
Guan, Zhi-Jin
Department of Computer Science
Institute of Electrical and Control Engineering
Brain Research Center
Keywords: Attribute reduction accelerator;bounded rationality region;distributed coevolutionary cloud;equilibrium dominance strategy;MapReduce framework
Issue Date: Mar-2016
Abstract: Aimed at the tremendous challenge of attribute reduction for big data mining and knowledge discovery, we propose a new attribute equilibrium dominance reduction accelerator (DCCAEDR) based on the distributed coevolutionary cloud model. First, the framework of N-populations distributed coevolutionary MapReduce model is designed to divide the entire population into N subpopulations, sharing the reward of different subpopulations\' solutions under a MapReduce cloud mechanism. Because the adaptive balancing between exploration and exploitation can be achieved in a better way, the reduction performance is guaranteed to be the same as those using the whole independent data set. Second, a novel Nash equilibrium dominance strategy of elitists under the N bounded rationality regions is adopted to assist the subpopulations necessary to attain the stable status of Nash equilibrium dominance. This further enhances the accelerator\'s robustness against complex noise on big data. Third, the approximation parallelism mechanism based on MapReduce is constructed to implement rule reduction by accelerating the computation of attribute equivalence classes. Consequently, the entire attribute reduction set with the equilibrium dominance solution can be achieved. Extensive simulation results have been used to illustrate the effectiveness and robustness of the proposed DCCAEDR accelerator for attribute reduction on big data. Furthermore, the DCCAEDR is applied to solve attribute reduction for traditional Chinese medical records and to segment
URI: http://dx.doi.org/10.1109/TSMC.2015.2464787
ISSN: 2168-2216
DOI: 10.1109/TSMC.2015.2464787
Volume: 46
Issue: 3
Begin Page: 384
End Page: 400
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