Abstract
An approach to the integration of multiple methods of user authentication and example of multi-classifier Bayesian and neural network is presented. The approach offers to find the convolution of outputs from multiple classifiers based on the complementary functions and to carry out the selection of the identification thresholds for each of the users. A number of complementary functions that use fundamentally different mathematical functions is analyzed. It is shown the practical need in metaheuristic algorithms for selecting the identification thresholds by comparison with the classic gradient method. The effectiveness some of the proposed series of multi-function, compared with the single use Bayes classifier and neural network is showed.
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Acknowledgments
This work was supported by the Ministry of Education and Science of the Russian Federation within 1.3 federal program research and development in priority areas of scientific-technological complex of Russia for 2014-2020 (grant agreement 14.577.21.0172 on October 27, 2015; identifier RFMEFI57715X0172).
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Kostyuchenko, E., Gurakov, M., Krivonosov, E., Tomyshev, M., Mescheryakov, R., Hodashinskiy, I. (2016). Integration of Bayesian Classifier and Perceptron for Problem Identification on Dynamics Signature Using a Genetic Algorithm for the Identification Threshold Selection. In: Cheng, L., Liu, Q., Ronzhin, A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science(), vol 9719. Springer, Cham. https://doi.org/10.1007/978-3-319-40663-3_71
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DOI: https://doi.org/10.1007/978-3-319-40663-3_71
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