Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Genetic Algorithm-based Variable Selection Approach for High-Growth Firm Prediction
Blekinge Institute of Technology, Faculty of Engineering, Department of Industrial Economics.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0001-7536-3349
KTO Karatay University, TUR.
Blekinge Institute of Technology, Faculty of Engineering, Department of Industrial Economics.ORCID iD: 0000-0002-0302-6244
Show others and affiliations
2022 (English)In: International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022, Institute of Electrical and Electronics Engineers (IEEE), 2022Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we propose a novel method for high-growth firm prediction by minimizing a cost function using a Genetic Algorithm (GA). To achieve it, the GA is used to search to find a set of important variables which provide the best fit for machine learning models so that accurate predictions can be made for high-growth firm prediction. The GA is employed to optimize the mean square error (MSE) between the accurate results and the predicted results of the machine learning methods by evolving the initially generated binary solutions through iterations. The proposed method obtains the best fitting set of variables for the machine learning methods for high-growth firm prediction. Four different machine learning methods which are Support Vector Machines (SVM), Logistic Regression, Random Forest (RF) and K-Nearest Neighbor (K-NN) have been employed with the GA and experimental results show that using RF with the GA achieves the best accuracy results with 94.93%. © 2022 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022.
Keywords [en]
Computational complexity, Cost functions, Genetic algorithms, Learning algorithms, Learning systems, Mean square error, Nearest neighbor search, Support vector regression, Complexity, Cost-function, High growth, High-growth firm prediction, Machine learning methods, Machine-learning, Novel methods, Optimisations, Random forests, Variables selections, Forecasting, Genetic algorithm, machine learning, optimization, variable selection
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-24243DOI: 10.1109/ICECCME55909.2022.9988729Scopus ID: 2-s2.0-85146429707ISBN: 9781665470957 (print)OAI: oai:DiVA.org:bth-24243DiVA, id: diva2:1731405
Conference
2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022, Male, 16 November through 18 November 2022
Available from: 2023-01-27 Created: 2023-01-27 Last updated: 2023-01-27Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Kusetogullari, AnnaKusetogullari, HüseyinAndersson, MartinEklund, Johan

Search in DiVA

By author/editor
Kusetogullari, AnnaKusetogullari, HüseyinAndersson, MartinEklund, Johan
By organisation
Department of Industrial EconomicsDepartment of Computer Science
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 61 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf