Issue |
E3S Web Conf.
Volume 224, 2020
Topical Problems of Agriculture, Civil and Environmental Engineering (TPACEE 2020)
|
|
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Article Number | 03006 | |
Number of page(s) | 13 | |
Section | Green IT Engineering | |
DOI | https://doi.org/10.1051/e3sconf/202022403006 | |
Published online | 23 December 2020 |
Machine learning as a tool for choice of enterprise development strategy
1
State University of Aerospace Instrumentation, 67, Bolshaya Morskaia st., St. Petersburg, 190000, Russia
2
National Research University Higher School of Economics, 3, Kantemirovskaya st., St. Petersburg, 194100, Russia
* Corresponding author: mkrichevsky@mail.ru
One of the main objectives of strategic management is the development and selection of strategies to achieve the desired results. The main goal of this paper is the analysis of the main domains or areas of machine learning application to support the process of strategic planning and decision making. The scientific methodology of the research studies is methods and procedures of modeling and intelligent analysis. This is theoretical and empirical paper in equal measure. This paper deals with the issues of machine learning implementation and how intellectual models and systems can be used to support the process of strategic planning. At the preprocessing stage on the basis of a modeled base of examples of strategy options, the use of clustering methods for forming groups of similar parameters that influence the choice of strategies and groups of similar enterprise objects, each of which has a certain type of strategy, is demonstrated. On the next step the selection of ranked characteristics that affect the choice of strategy is made. At the stage of solving the problem of choosing strategies, module Classifier Learner from MatLab 2018b is used.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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