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

Zekić-Sušac Marijana (), Pfeifer Sanja () and Šarlija Nataša ()
Additional contact information
Zekić-Sušac Marijana: University of Josip Juraj Strossmayer in Osijek, Faculty of Economics, Croatia
Pfeifer Sanja: University of Josip Juraj Strossmayer in Osijek, Faculty of Economics, Croatia
Šarlija Nataša: University of Josip Juraj Strossmayer in Osijek, Faculty of Economics, Croatia

Business Systems Research, 2014, vol. 5, issue 3, 82-96

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.

Keywords: machine learning; support vector machines; artificial neural networks; CART classification trees; k-nearest neighbour; large-dimensional data; crossvalidation (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://doi.org/10.2478/bsrj-2014-0021 (text/html)

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:bit:bsrysr:v:5:y:2014:i:3:p:82-96

DOI: 10.2478/bsrj-2014-0021

Access Statistics for this article

Business Systems Research is currently edited by Mirjana Pejić Bach

More articles in Business Systems Research from Sciendo
Bibliographic data for series maintained by Peter Golla ().