title: A hybrid method for classifying cognitive states from fMRI data.
abstract: Functional magnetic resonance imaging (fMRI) makes it possible to detect brain activities in order to elucidate cognitive-states. The complex nature of fMRI data requires under-standing of the analyses applied to produce possible avenues for developing models of cognitive state classification and improving brain activity prediction. While many models of classification task of fMRI data analysis have been developed, in this paper, we present a novel hybrid technique through combining the best attributes of genetic algorithms (GAs) and ensemble decision tree technique that consistently outperforms all other methods which are being used for cognitive-state classification. Specifically, this paper illustrates the combined effort of decision-trees ensemble and GAs for feature selection through an extensive simulation study and discusses the classification performance with respect to fMRI data. We have shown that our proposed method exhibits significant reduction of the number of features with clear edge classification accuracy over ensemble of decision-trees.
authors.affiliation: 1 Carrier Software and Core Network Department, Huawei Technologies India Pvt Ltd, Near EPIP Industrial Area, Whitefield Bangalore-560 066, Karnataka, India.
authors.forename: S
authors.initials: S
authors.lastname: Parida
authors.affiliation: 2 Department of Systems Engineering, Ajou University, San 5, Woncheon-dong, Yeongtong-gu, Suwon 443-749, South Korea.
authors.forename: S
authors.initials: S
authors.lastname: Dehuri
authors.affiliation: 3 Department of Computer Science, Yonsei University, Seoul, South Korea.
authors.forename: S-B
authors.initials: SB
authors.lastname: Cho
authors.affiliation: 4 Department of Clinical Sciences, Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia.
authors.forename: L A
authors.initials: LA
authors.lastname: Cacha
authors.affiliation: 4 Department of Clinical Sciences, Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia.
authors.forename: R R
authors.initials: RR
authors.lastname: Poznanski
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003071:Cognition / Q000502:physiology*; D003663:Decision Trees; D006801:Humans; D007807:Language Tests; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009483:Neuropsychological Tests; D010364:Pattern Recognition, Visual / Q000502:physiology*; D010775:Photic Stimulation; D011932:Reading
keywords: Functional magnetic resonance imaging; cognitive state classification; decision tree ensemble; genetic algorithms