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Efficient classification system based on Fuzzy-Rough Feature Selection and Multitree Genetic Programming for intension pattern recognition using brain signal

Title
Efficient classification system based on Fuzzy-Rough Feature Selection and Multitree Genetic Programming for intension pattern recognition using brain signal
Authors
Lee, JH[Lee, Jong-Hyun]Anaraki, JR[Anaraki, Javad Rahimipour]Ahn, CW[Ahn, Chang Wook]An, J[An, Jinung]
DGIST Authors
An, J[An, Jinung]
Issue Date
2015-02-15
Citation
Expert Systems with Applications, 42(3), 1644-1651
Type
Article
Article Type
Article
Keywords
Brain SignalBrain SignalsClassification SystemFeature ExtractionFeature SelectionFuzzy-Rough SetsIntension RecognitionMulti-Tree GPMulti-Trees
ISSN
0957-4174
Abstract
Recently, many researchers have studied in engineering approach to brain activity pattern of conceptual activities of the brain. In this paper we proposed a intension recognition framework (i.e. classification system) for high accuracy which is based on Fuzzy-Rough Feature Selection and Multitree Genetic Programming. The enormous brain signal data measured by fNIRS are reduced by proposed feature selection and extracted the informative features. Also, proposed Multitree Genetic Programming use the remain data to construct the intension recognition model effectively. The performance of proposed classification system is demonstrated and compared with existing classifiers and unreduced dataset. Experimental results show that classification accuracy increases while number of features decreases in proposed system. © 2014 Elsevier Ltd. All rights reserved.
URI
http://hdl.handle.net/20.500.11750/1577
DOI
10.1016/j.eswa.2014.09.048
Publisher
Elsevier B.V.
Related Researcher
Files:
There are no files associated with this item.
Collection:
Convergence Research Center for Wellness1. Journal Articles


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