Classifying cognitive states of brain activity via one-class neural networks with feature selection by genetic algorithms

It is generally assumed that one-class machine learning techniques can not reach the performance level of two-class techniques. The importance of this work is that while one-class is often the appropriate classification setting for identifying cognitive brain functions, most work in the literature has focused on two-class methods. In this paper, we demonstrate how one-class recognition of cognitive brain functions across multiple subjects can be performed at the 90% level of accuracy via an appropriate choice of features which can be chosen automatically. Our work extends one-class work by Hardoon and Manevitz (fMRI analysis via one-class machine learning techniques. In: Proceedings of the Nineteenth IJCAI, pp 1604–1605, 2005), where such classification was first shown to be possible in principle albeit with an accuracy of about 60%. The results of this paper are also comparable to work of various groups around the world e.g. Cox and Savoy (NeuroImage 19:261–270, 2003), Mourao-Miranda et al. (NeuroImage, 2006) and Mitchell et al., (Mach Learn 57:145–175, 2004) which have concentrated on two-class classification. The strengthening in the feature selection was accomplished by the use of a genetic algorithm run inside the context of a wrapper approach around a compression neural network for the basic one-class identification. In addition, versions of one-class SVM due to Scholkopf et al. (Estimating the support of a high-dimensional distribution. Technical Report MSR-TR-99-87, Microsoft Research, 1999) and Manevitz and Yousef (J Mach Learn Res 2:139–154, 2001) were investigated.

[1]  T. Carlson,et al.  Patterns of Activity in the Categorical Representations of Objects , 2003 .

[2]  R. Savoy.  Functional Magnetic Resonance Imaging (fMRI) , 2002 .

[3]  Tom M. Mitchell,et al.  Learning to Decode Cognitive States from Brain Images , 2004, Machine Learning.

[4]  Janaina Mourão Miranda,et al.  The impact of temporal compression and space selection on SVM analysis of single-subject and multi-subject fMRI data , 2006, NeuroImage.

[5]  Yiyu Yao,et al.  Brain activation detection by neighborhood one-class SVM , 2007, Cognitive Systems Research.

[6]  Malik Yousef,et al.  One-class document classification via Neural Networks , 2007, Neurocomputing.

[7]  Nathalie Japkowicz,et al.  A Novelty Detection Approach to Classification , 1995, IJCAI.

[8]  João Ricardo Sato,et al.  An fMRI normative database for connectivity networks using one‐class support vector machines , 2009, Human brain mapping.

[9]  David R. Hardoon,et al.  fMRI Analysis via One-class Machine Learning Techniques , 2005, IJCAI.

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

[11]  Garrison W. Cottrell,et al.  Image compression by back-propagation: An example of extensional programming , 1988 .

[12]  Rafael Malach,et al.  Large-Scale Mirror-Symmetry Organization of Human Occipito-Temporal Object Areas , 2003, Neuron.

[13]  M. Torrens.  Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268 , 1990 .

[14]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[15]  Talma Hendler,et al.  Center–periphery organization of human object areas , 2001, Nature Neuroscience.

[16]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[17]  N. Kanwisher,et al.  The Fusiform Face Area: A Module in Human Extrastriate Cortex Specialized for Face Perception , 1997, The Journal of Neuroscience.

[18]  Malik Yousef,et al.  One-Class SVMs for Document Classification , 2002, J. Mach. Learn. Res..

[19]  Tom M. Mitchell,et al.  Machine learning classifiers and fMRI: A tutorial overview , 2009, NeuroImage.

[20]  David D. Cox,et al.  Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex , 2003, NeuroImage.