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Abstract
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.
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Authors and Affiliations
Computer Science Department, University of Haifa , 31905, Haifa, Israel
Omer Boehm & Larry M. Manevitz
Department of Computer Science, University College London, London, UK
David R. Hardoon
School of Computing, National University of Singapore, Singapore, Singapore
David R. Hardoon
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Correspondence toLarry M. Manevitz.
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Boehm, O., Hardoon, D.R. & Manevitz, L.M. Classifying cognitive states of brain activity via one-class neural networks with feature selection by genetic algorithms.Int. J. Mach. Learn. & Cyber.2, 125–134 (2011). https://doi.org/10.1007/s13042-011-0030-3
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