A hybrid method for classifying cognitive states from fMRI data.

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.

[1]  Peter A Bandettini,et al.  Seven topics in functional magnetic resonance imaging. , 2009, Journal of integrative neuroscience.

[2]  Lawrence O. Hall,et al.  A Comparison of Decision Tree Ensemble Creation Techniques , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Hamid Parvin,et al.  Classification Ensemble by Genetic Algorithms , 2011, ICANNGA.

[4]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[5]  Ludmila I. Kuncheva,et al.  Naive random subspace ensemble with linear classifiers for real-time classification of fMRI data , 2012, Pattern Recognit..

[6]  Xinghua Lu,et al.  Feature selection for fMRI-based deception detection , 2009, BMC Bioinformatics.

[7]  Bogdan Gabrys,et al.  Genetic algorithms in classifier fusion , 2006, Appl. Soft Comput..

[8]  Runze Li,et al.  Statistical Challenges with High Dimensionality: Feature Selection in Knowledge Discovery , 2006, math/0602133.

[9]  M. Forsting,et al.  Functional segregation in the left premotor cortex in language processing: evidence from fMRI. , 2013, Journal of integrative neuroscience.

[10]  Matthew N. Anyanwu,et al.  Comparative Analysis of Serial Decision Tree Classification Algorithms , 2009 .

[11]  Thomas G. Dietterich.  An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[12]  A. Kharrat,et al.  A Hybrid Approach for Automatic Classification of Brain MRI Using Genetic Algorithm and Support Vector Machine , 2010 .

[13]  Michel Verleysen,et al.  The stability of feature selection and class prediction from ensemble tree classifiers , 2012, ESANN.

[14]  Zili Zhang,et al.  An Ensemble of Classifiers with Genetic Algorithm Based Feature Selection , 2008, IEEE Intell. Informatics Bull..

[15]  D. Opitz,et al.  Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..

[16]  Lawrence O. Hall,et al.  A Comparison of Decision Tree Ensemble Creation Techniques , 2007 .

[17]  Sam Kwong,et al.  Genetic algorithms: concepts and applications [in engineering design] , 1996, IEEE Trans. Ind. Electron..

[18]  Peter A. Bandettini,et al.  Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images , 2012, NeuroImage.

[19]  Wei Tang,et al.  Selective Ensemble of Decision Trees , 2003, RSFDGrC.

[20]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[21]  Dimitri Van De Ville,et al.  Decoding brain states from fMRI connectivity graphs , 2011, NeuroImage.

[22]  Lior Rokach,et al.  Ensemble Methods for Classifiers , 2005, The Data Mining and Knowledge Discovery Handbook.

[23]  Lior Rokach,et al.  Ensemble-based classifiers , 2010, Artificial Intelligence Review.

[24]  David R. Hardoon,et al.  Classifying cognitive states of brain activity via one-class neural networks with feature selection by genetic algorithms , 2011, Int. J. Mach. Learn. Cybern..

[25]  Ludmila I Kuncheva,et al.  Classifier ensembles for fMRI data analysis: an experiment. , 2010, Magnetic resonance imaging.

[26]  Durgesh Sharma,et al.  GA Based Scheduling of FMS Using Roulette Wheel Selection Process , 2011, SocProS.

[27]  R. R. Poznanski,et al.  Associable representations as field of influence for dynamic cognitive processes. , 2011, Journal of integrative neuroscience.

[28]  Tim Curran,et al.  Optimization of contrast detection power with probabilistic behavioral information , 2012, NeuroImage.

[29]  Lakhmi C. Jain,et al.  Designing classifier fusion systems by genetic algorithms , 2000, IEEE Trans. Evol. Comput..

[30]  Dimitri Van De Ville,et al.  Brain decoding of fMRI connectivity graphs using decision tree ensembles , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[31]  Albert Y. Zomaya,et al.  A Review of Ensemble Methods in Bioinformatics , 2010, Current Bioinformatics.

[32]  Sam Kwong,et al.  Genetic algorithms: concepts and applications [in engineering design] , 1996, IEEE Trans. Ind. Electron..

[33]  Tony R. Martinez,et al.  Decision Tree Ensemble: Small Heterogeneous Is Better Than Large Homogeneous , 2008, 2008 Seventh International Conference on Machine Learning and Applications.

[34]  Luca Scrucca,et al.  GA: A Package for Genetic Algorithms in R , 2013 .