Ensemble Learning in Investment Appraisal

Mikhail Krichevsky and Julia Martynova ()
Additional contact information
Mikhail Krichevsky: Saint Petersburg State University of Aerospace Instrumentation (SUAI)
Julia Martynova: Saint Petersburg State University of Aerospace Instrumentation (SUAI)

A chapter in Digital Technologies in Teaching and Learning Strategies, 2022, pp 244-253 from Springer

Abstract: Abstract The work is a continuation of the previous research of the authors. In this paper, the ensemble learning method was chosen from the machine learning methods, the essence of which is to use several models (“weak learners”) to solve the same problem. The main principle of the ensemble learning model is that combining weak learners forms a strong learner, thereby increasing the model’s accuracy. As a base of examples necessary for constructing a model, we used data on investment activities in various regions of Russia. Previously, the initial data were divided into several classes using the method of hierarchical clustering. To eliminate the significant imbalance in the original sample, various classification algorithms were used. The solution to the problem was obtained using the Matlab 2018b package, in which the Classifier Learner module was selected, which automatically achieves the result by various methods of ensemble learning. Among the five considered models, the RUSBoosted Trees method showed the best accuracy. The possibility of classifying regions by this method by assigning them to a predetermined class is shown, an error matrix and an operational characteristic are found. The proposed method can be applied in other management tasks related to the assessment of innovations and investments.

Keywords: Ensemble learning; Investment activities; Matlab package; Method RUSBoosted Trees (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-031-05175-3_25

Ordering information: This item can be ordered from
http://www.springer.com/9783031051753

DOI: 10.1007/978-3-031-05175-3_25

Access Statistics for this chapter

More chapters in Lecture Notes in Information Systems and Organization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().