Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 42, Issue 3, Pages 1644-1651Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2014.09.048
Keywords
Fuzzy-rough sets; Feature selection; Multitree GP; Brain signal; Intension recognition
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Funding
- National Research Foundation of Korea (NRF) grant - Korean government (MEST) [NRF-2012R1A2A2A01013735]
- DGIST R&D Program of the Ministry of Science, ICT and Future Planning [14-RS-02]
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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. (C) 2014 Elsevier Ltd. All rights reserved.
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