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Pregled bibliografske jedinice broj: 717839

A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem


Zekić-Sušac, Marijana; Pfeifer, Sanja; Šarlija, Nataša
A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem // Business Systems Research, 5 (2014), 3; 82-96 doi:10.2478/bsrj-2014-0021 (međunarodna recenzija, članak, znanstveni)


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Naslov
A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

Autori
Zekić-Sušac, Marijana ; Pfeifer, Sanja ; Šarlija, Nataša

Izvornik
Business Systems Research (1847-8344) 5 (2014), 3; 82-96

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
machine learning ; support vector machines ; artificial neural networks ; CART classification trees ; k-nearest neighbour ; large-dimensional data ; cross-validation

Sažetak
Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross- validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.

Izvorni jezik
Engleski

Znanstvena područja
Ekonomija, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Ustanove:
Ekonomski fakultet, Osijek

Citiraj ovu publikaciju:

Zekić-Sušac, Marijana; Pfeifer, Sanja; Šarlija, Nataša
A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem // Business Systems Research, 5 (2014), 3; 82-96 doi:10.2478/bsrj-2014-0021 (međunarodna recenzija, članak, znanstveni)
Zekić-Sušac, M., Pfeifer, S. & Šarlija, N. (2014) A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem. Business Systems Research, 5 (3), 82-96 doi:10.2478/bsrj-2014-0021.
@article{article, year = {2014}, pages = {82-96}, DOI = {10.2478/bsrj-2014-0021}, keywords = {machine learning, support vector machines, artificial neural networks, CART classification trees, k-nearest neighbour, large-dimensional data, cross-validation}, journal = {Business Systems Research}, doi = {10.2478/bsrj-2014-0021}, volume = {5}, number = {3}, issn = {1847-8344}, title = {A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem}, keyword = {machine learning, support vector machines, artificial neural networks, CART classification trees, k-nearest neighbour, large-dimensional data, cross-validation} }
@article{article, year = {2014}, pages = {82-96}, DOI = {10.2478/bsrj-2014-0021}, keywords = {machine learning, support vector machines, artificial neural networks, CART classification trees, k-nearest neighbour, large-dimensional data, cross-validation}, journal = {Business Systems Research}, doi = {10.2478/bsrj-2014-0021}, volume = {5}, number = {3}, issn = {1847-8344}, title = {A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem}, keyword = {machine learning, support vector machines, artificial neural networks, CART classification trees, k-nearest neighbour, large-dimensional data, cross-validation} }

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Emerging Sources Citation Index (ESCI)


Uključenost u ostale bibliografske baze podataka::


  • Cabells' DIrectory, Celdes, CNKI Scholar, CNPIEC, DOAJ, EBSCO Discovery Service, Elsevier-Scirus, Google Scholar, Hrcak, Proquest, RePec, Summon, TDOne (TDNet), TEMA Technik und Management, Ulrich's Periodicals Directory, WorldCat (OCLC)


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