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

Zekić-Sušac Marijana (), Pfeifer Sanja () and Šarlija Nataša ()
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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
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Persistent link: https://EconPapers.repec.org/RePEc:bit:bsrysr:v:5:y:2014:i:3:p:82-96

DOI: 10.2478/bsrj-2014-0021

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