| Title: | Comparative analysis of nonlinear models developed using machine learning algorithms |
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| Authors: | ID Rožman, Maja (Author)
ID Kišić, Alen (Author)
ID Oreški, Dijana (Author) |
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| Files: |  https://wseas.com/journals/isa/2024/a565109-016(2024).pdf
 Comparative_Analysis_of_Nonlinear_Models_Developed_using_Machine.pdf (730,52 KB)
MD5: 654FB317F175BE6570CA7962548CAD9D
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | EPF - Faculty of Business and Economics
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| Abstract: | Machine learning algorithms are increasingly used in a vast spectrum of domains where statistical approaches were previously used. Algorithms such as artificial neural networks, classification, regression trees, or support vector machines provide various advantages over traditional linear regression or discriminant analysis. Advantages such as flexibility, scalability, and improved accuracy in dealing with diverse data types, nonlinear problems, and dimensionality reduction, compared to traditional statistical methods are empirically demonstrated in many previous research papers. In this paper, two machine learning algorithms are compared with one statistical method on highly nonlinear data. Results indicate a high level of effectiveness for machine learning algorithms when dealing with nonlinearity. |
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| Keywords: | machine learning, decision tree algorithm, artificial neural network, predictive models, data characteristics, nonlinear data, artificial intelligence |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 18.07.2023 |
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| Article acceptance date: | 16.05.2024 |
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| Publication date: | 20.06.2024 |
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| Publisher: | WSEAS |
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| Year of publishing: | 2024 |
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| Number of pages: | str. 303-307 |
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| Numbering: | Vol. 21, [art. no.] 29 |
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| PID: | 20.500.12556/DKUM-92131  |
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| UDC: | 007 |
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| ISSN on article: | 2224-3402 |
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| COBISS.SI-ID: | 199795971 |
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| DOI: | 10.37394/23209.2024.21.29 |
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| Publication date in DKUM: | 02.07.2025 |
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| Views: | 203 |
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| Downloads: | 13 |
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| Metadata: |    |
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| Categories: | Misc.
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