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Authors: Meshalkin V. P., Dli M. I., Puchkov A., Rysina (Lobaneva) E.     Published in № 3(93) 30 june 2021 year
Rubric: Models and Methods

Preliminary assessment of the pragmatic value of information in the classifiсation problem based on deep neural networks

A method is proposed for preliminary assessment of the pragmatic value of information in the problem of classifying the state of an object based on deep recurrent networks of long short-term memory. The purpose of the study is to develop a method for predicting the state of a controlled object while minimizing the number of used prognostic parameters through a preliminary assessment of the pragmatic value of information. This is an especially urgent task under conditions of processing big data, characterized not only by significant volumes of incoming information, but also by information rate and multiformatness. The generation of big data is now happening in almost all areas of activity due to the widespread introduction of the Internet of Things in them. The method is implemented by a two-level scheme for processing input information. At the first level, a Random Forest machine learning algorithm is used, which has significantly fewer adjustable parameters than a recurrent neural network used at the second level for the final and more accurate classification of the state of the controlled object or process. The choice of Random Forest is due to its ability to assess the importance of variables in regression and classification problems. This is used in determining the pragmatic value of the input information at the first level of the data processing scheme. For this purpose, a parameter is selected that reflects the specified value in some sense, and based on the ranking of the input variables by the level of importance, they are selected to form training datasets for the recurrent network. The algorithm of the proposed data processing method with a preliminary assessment of the pragmatic value of information is implemented in a program in the MatLAB language, and it has shown its efficiency in an experiment on model data.

Key words

decision trees, deep recurrentneural networks

The author:

Meshalkin V. P.

Degree:

Academician of the Russian Academy of Sciences, Dr. Sci. (Eng.), Professor, Head of the Logistics and Economic Information Department, Mendeleev University of Chemical Technology of Russia, Moscow; Laboratory of Physical and Chemical Fundamentals of Chromatography and Chromato-Mass Spectrometry, Institute of Physical Chemistry and Electrochemistry named after A.N. Frumkin of the Russian Academy of Sciences, Moscow; Department of Computer-Aided Design and Control Systems, Saint Petersburg State Institute of Technology (Technical University)

Location:

Saint Petersburg, Russia

The author:

Dli M. I.

Degree:

Dr. Sci. (Eng.), Professor, Information Technologies in Economics and Management Department, Branch of the National Research University “MPEI” in Smolensk, Smolensk; Leading Researcher, Synergy University

Location:

Smolensk, Russia

The author:

Puchkov A.

Degree:

Cand. Sci. (Eng.), Associate Professor, Information Technologies in Economics and Management Department, Branch of the National Research University “MPEI” in Smolensk

Location:

Smolensk, Russia

The author:

Rysina (Lobaneva) E.

Degree:

Postgraduate, Applied Mathematics and Artificial Intelligence Department, National Research University "MPEI"

Location:

Moscow, Russia