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

Author

Listed:
  • Zekić-Sušac Marijana

    ()

  • Pfeifer Sanja

    ()

  • Šarlija Nataša

    () (University of Josip Juraj Strossmayer in Osijek, Faculty of Economics, Croatia)

Abstract

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.

Suggested Citation

  • Zekić-Sušac Marijana & Pfeifer Sanja & Šarlija Nataša, 2014. "A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem," Business Systems Research, Sciendo, vol. 5(3), pages 82-96, September.
  • Handle: RePEc:bit:bsrysr:v:5:y:2014:i:3:p:82-96
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    References listed on IDEAS

    as
    1. Carr, Jon C. & Sequeira, Jennifer M., 2007. "Prior family business exposure as intergenerational influence and entrepreneurial intent: A Theory of Planned Behavior approach," Journal of Business Research, Elsevier, vol. 60(10), pages 1090-1098, October.
    2. Bolivar-Cime, A. & Marron, J.S., 2013. "Comparison of binary discrimination methods for high dimension low sample size data," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 108-121.
    3. KruegerJR, Norris F. & Reilly, Michael D. & Carsrud, Alan L., 2000. "Competing models of entrepreneurial intentions," Journal of Business Venturing, Elsevier, vol. 15(5-6), pages 411-432.
    4. Kolvereid, Lars & Isaksen, Espen, 2006. "New business start-up and subsequent entry into self-employment," Journal of Business Venturing, Elsevier, vol. 21(6), pages 866-885, November.
    5. Joyce Koe Hwee Nga & Gomathi Shamuganathan, 2010. "The Influence of Personality Traits and Demographic Factors on Social Entrepreneurship Start Up Intentions," Journal of Business Ethics, Springer, vol. 95(2), pages 259-282, August.
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