Krichevskiy M.L., Martynova Yu.A.Published in:
Russian Journal of Innovation Economics
– Volume 9, Number 4 (October-December 2019)

JEL classification: D81, С45, С65

Keywords: classification methods, cluster analysis, determining the class of regions, Key words: investment activity, machine learning


Share:

Abstract:

The results of the application of machine learning methods suitable for evaluating the investment activity of various regions of Russia are presented. The database used in this work was the Rosstat report for 2018, which contains information on the investment activity of all Russian regions. The solution to the problem is brought to the receipt of information about the class to which this or that region belongs. The machine learning algorithms used in the work were taken from the software product MatLab 2018b. As a result of the study, in order to solve the problem, the best methods for classification accuracy were selected, with which you can judge the activities of Russian regions in the field of investment. It is shown that the results obtained are used to form an assessment of whether a new observation belongs to a specific category.

---

References:
(2018). Investitsionnaya deyatelnost v Rossii: usloviya, faktory, tendentsii [Investment activity in Russia: conditions, factors, tendencies] (in Russian).
Alpaydin E. (2010). Introduction to machine learning. Massachusetts Institute of Technology
Azad M., Moshkov M. (2017). Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions European Journal of Operational Research. 263 (3). 910-921. doi: 10.1016/j.ejor.2017.06.026.
Chandrinos S.K., Sakkas G., Lagaros N.D. (2018). AIRMS: A risk management tool using machine learning Expert Systems with Applications. 105 (9). 34-48.
Daumé H. (2012). A Course in Machine Learning
Everitt B.S., Landau S., Leese M. et al. (2011). Cluster Analysis
Ezghazi S., Zahi A., Zekoua K. (2017). A new nearest neighbor classification method based on fuzzy set theory and aggregation operators Expert Systems with Applications. 80 (1). 58-74.
Gao W., Alsarraf J., Moayedi H. et al. (2019). Comprehensive preference learning and feature validity for designing energy-efficient residential buildings using machine learning paradigms Applied Soft Computing. 84 (11). art.105748.
Kaufman L., Rousseeuw P.J. (2005). Finding Groups in Data: An Introduction to Cluster Analysis
Kim K., Hong J. (2017). A hybrid decision tree algorithm for mixed numeric and categorical data in regression analysis Pattern Recognition Letters. (98). 39-45. doi: 10.1016/j.patrec.2017.08.011.
Krichevskiy M.L., Martynova Yu.A. (2017). Otsenka investitsionnoy deyatelnosti regionov Rossii [Assessment of investment activity of Russia regions] Sustainable development of the regions of Russia: from strategy to tactics. 65-71. (in Russian).
Milskaya E.A., Bychkova A.V. (2017). Analiz i otsenka potentsiala innovatsionno-investitsionnoy deyatelnosti ekonomicheskikh subektov (na primere Severo-Zapadnogo federalnogo okruga) [Analysis and evaluation of the potential of innovation and investment activities of economic entities (on the example of the North-West Federal district)]. St. Petersburg Polytechnic University Journal of Engineering Science and Technology. 10 (2). (in Russian). doi: 10.18721/JE.10204 .
Portugal I., Alencar P., Cowan D. (2018). The use of machine learning algorithms in recommender systems: A systematic review Expert Systems with Applications. 97 (5). 205-227.
Shalev-Shwartz S., Ben-David S. (2014). Understanding Machine Learning: From Theory to Algorithms
Stanula P., Ziegenbein A., Metternich J. (2018). Мachine learning algorithms in production: A guideline for efficient data source selection Procedia CIRP. (78). 261-266.
Tavernier J., Simm J., Meerbergen K. et al. (2019). Fast semi-supervised discriminant analysis for binary classification of large data sets Pattern Recognition. 91 (7). 86-99.
Vapnik V. N., Chervonenkis A. Ya. (1974). Eoriya raspoznavaniya obrazov [Theory of pattern recognition] Moskva : Nauka. (in Russian).
Vapnik V.N. (1998). Statistical Learning Theory
ZhangX., Li Y., ZhangX (2017). KRNN: k Rare-class, Nearest Neighbour classification Pattern Recognition. (62). 33-44. doi: 10.1016/j.patcog.2016.08.023.