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Feature Selection in Neural Network Solution of Inverse Problem Based on Integration of Optical Spectroscopic Methods

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Book cover Advances in Neural Computation, Machine Learning, and Cognitive Research IV (NEUROINFORMATICS 2020)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 925))

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Abstract

This study considers a neural network solution to the inverse problem based on the integration of optical spectroscopy methods for determining ion concentrations in aqueous solutions. The effect of integration of physical methods is studied using the selection of significant input features. The previously formulated thesis is confirmed that if the integrated methods differ much by their accuracy, then the integration of these methods is ineffective. Use of joint selection of significant input features may only slightly improve the quality of solution in a limited number of situations. Among the tested methods of feature selection, embedded method based on the analysis of weights of an already trained neural network (multi-layer perceptron) gives better results than filter methods with selection based on standard deviation, cross-correlation, or cross-entropy, at the expense of some additional computation. Selection of the significant input features allows improving the quality of the ANN solution relative to the solution obtained on the full sets of features. The improvement is the greater, the worse is the initial solution.

This study has been performed at the expense of the grant of Russian Science Foundation (project no. 19-11-00333).

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Authors and Affiliations

  1. D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Moscow, Russia

    Igor Isaev, Olga Sarmanova, Sergey Burikov, Tatiana Dolenko, Kirill Laptinskiy & Sergey Dolenko

  2. Faculty of Physics, M.V. Lomonosov Moscow State University, Moscow, Russia

    Olga Sarmanova, Sergey Burikov & Tatiana Dolenko

Authors
  1. Igor Isaev
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  2. Olga Sarmanova
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  3. Sergey Burikov
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  4. Tatiana Dolenko
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  5. Kirill Laptinskiy
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  6. Sergey Dolenko
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Corresponding author

Correspondence to Igor Isaev .

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Editors and Affiliations

  1. Scientific Research Institute for System Analysis, Russian Academy of Sciences, Moscow, Russia

    Boris Kryzhanovsky

  2. Scientific Research Institute for System Analysis, Russian Academy of Sciences, Moscow, Russia

    Witali Dunin-Barkowski

  3. Scientific Research Institute for System Analysis, Russian Academy of Sciences, Moscow, Russia

    Vladimir Redko

  4. Moscow Aviation Institute (National Research University), Moscow, Russia

    Yury Tiumentsev

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Isaev, I., Sarmanova, O., Burikov, S., Dolenko, T., Laptinskiy, K., Dolenko, S. (2021). Feature Selection in Neural Network Solution of Inverse Problem Based on Integration of Optical Spectroscopic Methods. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research IV. NEUROINFORMATICS 2020. Studies in Computational Intelligence, vol 925. Springer, Cham. https://doi.org/10.1007/978-3-030-60577-3_27

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