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Feature Selection with a Genetic Algorithm for Classification of Brain Imaging Data

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Advances in Feature Selection for Data and Pattern Recognition

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 138))

Abstract

Recent advances in brain imaging technology, coupled with large-scale brain research projects, such as the BRAIN initiative in the U.S. and the European Human Brain Project, allow us to capture brain activity in unprecedented details. In principle, the observed data is expected to substantially shape our knowledge about brain activity, which includes the development of new biomarkers of brain disorders. However, due to the high dimensionality, the analysis of the data is challenging, and selection of relevant features is one of the most important analytic tasks. In many cases, due to the complexity of search space, evolutionary algorithms are appropriate to solve the aforementioned task. In this chapter, we consider the feature selection task from the point of view of classification tasks related to functional magnetic resonance imaging (fMRI) data. Furthermore, we present an empirical comparison of conventional LASSO-based feature selection and a novel feature selection approach designed for fMRI data based on a simple genetic algorithm.

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Notes

  1. 1.

    The accuracy for the fitness function of mGA was calculated solely on the training data. In particular we measured the accuracy of a nearest neighbor classifier in an internal 5-fold cross-validation on the training data.

  2. 2.

    We note that \(\lambda = 0.001\) and \(\lambda = 0.0001\) led to very similar classification accuracy. For simplicity, we only show the results in case of \(\lambda = 0.005\) in Sect. 10.3.

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Acknowledgements

This work partially was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS-UEFISCDI, project number PN-II-RU-TE-2014-4-2332 and the National Research, Development and Innovation Office (Hungary), project number: NKFIH 108947 K.

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Authors and Affiliations

  1. Centre for the Study of Complexity, Babeş-Bolyai University, str. Kogalniceanu, Nr.1, Cluj-Napoca, Romania

    Annamária Szenkovits, Noémi Gaskó, Rodica Ioana Lung & Mihai Suciu

  2. Department of Cognitive Science, Budapest University of Technology and Economics, Egry József utca 1, Budapest, 1111, Hungary

    Regina Meszlényi

  3. Brain Imaging Centre, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok krt. 2, Budapest, 1117, Hungary

    Regina Meszlényi

  4. Knowledge Discovery and Machine Learning, Institute für Informatik III, Rheinische Friedrich-Wilhelms-Universität Bonn, Römerstr. 164, 53117, Bonn, Germany

    Krisztian Buza

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  1. Annamária Szenkovits
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  2. Regina Meszlényi
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Correspondence to Annamária Szenkovits .

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  1. Silesian University of Technology , Gliwice, Poland

    Urszula Stańczyk

  2. University of Silesia in Katowice , Katowice, Poland

    Beata Zielosko

  3. University of Bournemouth , Poole, United Kingdom

    Lakhmi C. Jain

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Szenkovits, A., Meszlényi, R., Buza, K., Gaskó, N., Lung, R.I., Suciu, M. (2018). Feature Selection with a Genetic Algorithm for Classification of Brain Imaging Data. In: Stańczyk, U., Zielosko, B., Jain, L. (eds) Advances in Feature Selection for Data and Pattern Recognition. Intelligent Systems Reference Library, vol 138. Springer, Cham. https://doi.org/10.1007/978-3-319-67588-6_10

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