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Digital Library

of the European Council for Modelling and Simulation

 

Title:

Probability Model Of Concepts Recovery In Small Sample Learning

Authors:

Alexander A. Grusho, Nick A. Grusho, Michael I. Zabezhailo, Elena E. Timonina, Vladislav V. Kulchenkov

Published in:

 

 

2020). ECMS 2020 Proceedings Edited by: Mike Steglich, Christian Muller, Gaby Neumann, Mathias Walther, European Council for Modeling and Simulation.

 

DOI: http://doi.org/10.7148/2020

ISSN: 2522-2422 (ONLINE)

ISSN: 2522-2414 (PRINT)

ISSN: 2522-2430 (CD-ROM)

 

ISBN: 978-3-937436-68-5
ISBN: 978-3-937436-69-2(CD)

 

Communications of the ECMS , Volume 34, Issue 1, June 2020,

United Kingdom

 

Citation format:

Alexander A. Grusho, Nick A. Grusho, Michael I. Zabezhailo, Elena E. Timonina, Vladislav V. Kulchenkov (2020). Probability Model Of Concepts Recovery In Small Sample Learning, ECMS 2020 Proceedings Edited By: Mike Steglich, Christian Mueller, Gaby Neumann, Mathias Walther European Council for Modeling and Simulation. doi: 10.7148/2020-0393

DOI:

https://doi.org/10.7148/2020-0393

Abstract:

Many information security monitoring systems and con-trolling of IoT systems receive information in the form of short messages, which can be considered as small sam-ples. Concepts are considered as classes of small samples that allow you to determine the correctness of monitor-ing systems. The paper is devoted to the problem of recovering concepts on observations of series of small samples.

Probabilistic model of appearance of series of small samples is introduced. To define concepts, the proba-bilistic dependency is used within series of small sam-ples. The case of series of length 2 of small samples is considered. This assumption allowed the construc-tion of a random graph and provided its probability-statistical analysis. Asymptotic approximations of prob-ability distributions in the series scheme are used to identify ranges of parameter values that better define the structure of concepts. The set of parameter values is defined, at which the structure of concepts is uniquely determined with probability which tends to 1.

 

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