S. N. Yashina, b, L. P. Ziankov, E. V. Koshelev, A. A. Ivanov. Innovative Rating of Regions in the Electronic Industry: Construction and Verification Using Machine Learning

https://doi.org/10.15507/2413-1407.129.033.202504.678-696
EDN: https://elibrary.ru/ixslne
УДК / UDC 001.895:338.24

Abstract

Introduction. The development of the radio-electronic industry is a priority for Russia's technological leadership, necessitating modern tools for assessing the innovative potential of its regions. This study aims to construct and verify an innovative rating of regions for the radio-electronic industry that overcomes the limitations of traditional ratings by applying machine learning to Big Data.

Materials and Methods. A training dataset was formed based on Rosstat data from 2010–2022 for 83 regions. Using ensemble machine learning methods (Fine Gaussian SVM, Bagged Trees, Random Forest), a classification model was constructed that assigns innovative ratings (A – leaders, B – average level, C – depressed) to regions based on three target functions, with subsequent aggregation into an integral I-score. A key stage of the research was the model approbation: its verification was carried out on independent data for 2023 that was not part of the training set.

Results. The verification confirmed the model's practical applicability: the accuracy of the integral I-score rating prediction on new data was 81.93 %. Based on the approbation results, a current map of innovative ratings was constructed. The leading regions (A) in 2023 were the Moscow Region, Moscow, St. Petersburg, Republic of Tatarstan, Nizhny Novgorod Region, and Sverdlovsk Region. Analysis of discrepancies between prediction and fact revealed growth potential for Novosibirsk Region and potential risks to the leading positions of Republic of Bashkortostan, Perm Territory, and Chelyabinsk Region.

Discussion and Conclusion. The approbated methodology enables the construction of accurate and robust assessments of the innovative development of regions in the radio-electronic industry. The verification results demonstrate not only the model's predictive power but also its value for identifying latent trends. The findings are of practical importance for public authorities and large companies in planning regional and sectoral policies.

Keywords: radio-electronic industry, innovative regional rating, machine learning, classification, ensemble methods, model verification, Big Data, regional economy, Russian electronics industry

Conflict of interest. The authors declare no conflict of interest.

Funding. The article was supported by the Russian Science Foundation, grant 24-28-00464.

For citation: Yashin S.N., Ziankova L.P., Koshelev E.V., Ivanov A.A. Innovative Rating of Regions in the Electronic Industry: Construction and Verification Using Machine Learning. Russian Journal of Regional Studies. 2025;33(4):678–696. https://doi.org/10.15507/2413-1407.129.033.202504.678-696

REFERENCES

1. Zhikhareva A.K. Innovative Ratings of Russian Regions: Methodological Features of Their Formation and Practice of Application. The Bulletin of the Institute of Economics of the Russian Academy of Sciences. 2020;(2):121–136. (In Russ., abstract in Eng.) https://doi.org/10.24411/2073-6487-2020-10020

2. Shchepina I.N., Maslova M.I. Assessment of the Level of Innovative Development of Regions Taking into Account the Potential of Digitalization. Modern Economics: Problems and Solutions. 2022;(12):8–23. (In Russ., abstract in Eng.) https://doi.org/10.17308/meps.2021.12/2727

3. Dolgih E.A., Parshintseva L.S. Assessment of the Innovative Development of Russian Federation Regions. Finance and Management. 2024;(3):37–56. (In Russ., abstract in Eng.) https://doi.org/10.25136/2409-7802.2024.3.71213

4. Glezman L.V., Isaev S.Y., Fedoseeva S.S. Rating as a Method of Assessing Innovative and Scientific and Technological Development of Russian Regions. Russian Journal of Innovation Economics. 2023;13(2):927–940. (In Russ., abstract in Eng.) https://doi.org/10.18334/vinec.13.2.117950

5. Netrebin Yu.Yu., Ulyakina N.А., Vershinin I.V., Burdakova A.Е. Scientific, Technological and Innovative Capacity of the Region: Comparison of Current Approaches to Evaluation. Economics and Management: Problems, Solutions. 2020;1(10):107–116. (In Russ., abstract in Eng.) https://doi.org/10.36871/ek.up.p.r.2020.10.01.013

6. Yakovenko N.V., Semenova L.V., Nikolskaya E.Y. at al. Innovative Development of Russian Regions: Assessment and Dynamics in the Context of Sustainable Development. Sustainability. 2024;16(3):1271. https://doi.org/10.3390/su16031271

7. Szopik-Depczyńska K., Cheba K., Bąk I., Kędzierska-Szczepaniak A., Szczepaniak K., Ioppolo G. Innovation Level and Local Development of EU regions. A New Assessment Approach. Land Use Policy. 2020;(99):104837. https://doi.org/10.1016/j.landusepol.2020.104837

8. Hertrich T.J., Brenner T. Classification of Regions According to the Dominant Innovation Barriers: The Characteristics and Stability of Region Types in Germany. Regional Science Policy and Practice. 2023;15(9):2182–2224. https://doi.org/10.1111/rsp3.12711

9. Tran V.T., Vu T.T.H., Nguyen P.L. Factors Affecting the Development of the Supporting Industry for the Electronics Industry in Vietnam. Journal of Positive School Psychology. 2022;6(11):2394–2408. https://journalppw.com/index.php/jpsp/article/view/14449

10. Alnuaimi A., Albaldawi T. An Overview of Machine Learning Classification Techniques. BIO Web of Conferences. 2024;(97):00133. https://doi.org/10.1051/bioconf/20249700133

11. Sarker I.H. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science. 2021;(2):160. https://doi.org/10.1007/s42979-021-00592-x

12. Ghasemkhani B., Balbal K.F., Birant D. A New Predictive Method for Classification Tasks in Machine Learning: Multi-Class Multi-Label Logistic Model Tree (MMLMT). Mathematics. 2024;12(18):2825. https://doi.org/10.3390/math12182825

13. Tan H. Machine Learning Algorithm for Classification. Journal of Physics: Conference Series. 2021;1994(1):12–16. https://doi.org/10.1088/1742-6596/1994/1/012016

14. Lin S.-L. Application of Machine Learning to a Medium Gaussian Support Vector Machine in the Diagnosis of Motor Bearing Faults. Electronics. 2021;10(18):2266. https://doi.org/10.3390/electronics10182266

15. Genuer R., Poggi J.-M., Tuleau-Malot Ch., Villa-Vialanei N. Random Forests for Big Data. Big Data Research. 2017;(9):28–46. https://doi.org/10.1016/j.bdr.2017.07.003

16. Plaia A., Buscemi S., Fürnkranz J., Mencía E.L. Comparing Boosting and Bagging for Decision Trees of Rankings. Journal of Classification. 2022;(39):78–99. https://doi.org/10.1007/s00357-021-09397-2

17. Ibarguren I., Pérez J.M., Muguerza J., Arbelaitz O., Yera A. PCTBagging: From Inner Ensembles to Ensembles. A Trade-Off between Discriminating Capacity and Interpretability. Information Sciences. 2022;(583):219–238. https://doi.org/10.1016/j.ins.2021.11.010

18. Lee S., Bikash KC, Choeh J.Y. Comparing Performance of Ensemble Methods in Predicting Movie Box Office Revenue. Heliyon. 2020;6(6):e04260. https://doi.org/10.1016/j.heliyon.2020.e04260

19. Jafarzadeh H., Mahdianpari M., Gill E., Mohammadimanesh F., Homayouni S. Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation. Remote Sensing. 2021;13(21):4405. https://doi.org/10.3390/rs13214405

20. Ngo G., Beard R., Chandra R. Evolutionary Bagging for Ensemble Learning. Neurocomputing. 2022;(510):1–14. https://doi.org/10.1016/j.neucom.2022.08.055

About the authors:

Sergei N. Yashin, Dr.Sci. (Econ.), Professor, Head of the Chair of Management and Public Administration, Lobachevsky University (23 Prospekt Gagarina, 603022 Nizhny Novgorod, Russian Federation), Nizhny Novgorod State Technical University n.a. R.E. Alekseev (24 Minin St., 603155 Nizhny Novgorod, Russian Federation), ORCID: https://orcid.org/0000-0002-7182-2808, Researcher ID: O-1752-2014, Scopus ID: 57191255169, SPIN-code: 4191-7293, jashinsn@yandex.ru

Larysa P. Ziankova, Dr.Sci. (Econ.), Professor of the Chair of Economics and Management, Belarusian State Economic University (26 Partizanskii Prospekt, 220070 Minsk, Republic of Belarus), ORCID: https://orcid.org/0000-0002-1959-430X, Scopus ID: 57804766500, keu@bseu.by

Egor V. Koshelev, Cand.Sci. (Econ.), Associate Professor, Associate Professor of the Chair of Management and Public Administration, Lobachevsky University (23 Prospekt Gagarina, 603022 Nizhny Novgorod, Russian Federation), Nizhny Novgorod State Technical University n.a. R.E. Alekseev (24 Minin St., 603155 Nizhny Novgorod, Russian Federation), ORCID: https://orcid.org/0000-0001-5290-7913, Researcher ID: N-8586-2014, Scopus ID: 57192163661 SPIN-code: 8429-5702, ekoshelev@yandex.ru

Alexey A. Ivanov, Cand.Sci. (Econ.), Associate Professor, Associate Professor of the Chair of Management and Public Administration, Lobachevsky University (23 Prospekt Gagarina, 603022 Nizhny Novgorod, Russian Federation), Nizhny Novgorod State Technical University n.a. R.E. Alekseev (24 Minin St., 603155 Nizhny Novgorod, Russian Federation), ORCID: https://orcid.org/0000-0003-4299-4042, Researcher ID: F-1106-2014, Scopus ID: 57207917762, SPIN-code: 1055-4483, alexey.iff@yandex.ru

Contribution of the authors:

S. N. Yashin – ideas; formulation or evolution of overarching research goals and aims; funding acquisition; oversight and leadership responsibility for the research activity planning and execution, including mentorship external to the core team.

L. P. Ziankova – ideas; formulation or evolution of overarching research goals and aims; funding acquisition; oversight and leadership responsibility for the research activity planning and execution, including mentorship external to the core team.

E. V. Koshelev – preparation, creation of the published work, specifically visualization / data presentation.

A. A. Ivanov – preparation, creation of the published work, specifically visualization / data presentation.

Availability of data and materials. The datasets used and/or analyzed during the current study are available from the authors on reasonable request.

The authors have read and approved the final manuscript.

Submitted 07.04.2025 revised 08.10.2025; accepted 15.10.2025.