Original scientific paper
CLASSIFICATION OF ENTREPRENEURIAL INTENTIONS BY NEURAL NETWORKS, DECISION TREES AND SUPPORT VECTOR MACHINES
Marijana Zekić-Sušac
; Faculty of Economics, University of J.J. Strossmayer in Osijek, Croatia
Sanja Pfeifer
orcid.org/0000-0002-7394-3080
; Faculty of Economics, University of J.J. Strossmayer in Osijek, Croatia
Ivana Đurđević
; Faculty of Teacher Education, University of J.J. Strossmayer in Osijek, Croatia
Abstract
Entrepreneurial intentions of students are important to recognize during the study in order to provide those students with educational background that will support such intentions and lead them to successful entrepreneurship after the study. The paper aims to develop a model that will classify students according to their entrepreneurial intentions by benchmarking three machine learning classifiers: neural networks, decision trees, and support vector machines. A survey was conducted at a Croatian university including a sample of students at the first year of study. Input variables described students’ demographics, importance of business objectives, perception of entrepreneurial carrier, and entrepreneurial predispositions. Due to a large dimension of input space, a feature selection method was used in the pre-processing stage. For comparison reasons, all tested models were validated on the same out-of-sample dataset, and a cross-validation procedure for testing generalization ability of the models was conducted. The models were compared according to its classification accuracy, as well according to input variable importance. The results show that although the best neural network model produced the highest average hit rate, the difference in performance is not statistically significant. All three models also extract similar set of features relevant for classifying students, which can be suggested to be taken into consideration by universities while designing their academic
programs.
Keywords
classification; entrepreneurial intentions; decision trees; neural networks; support vector machines
Hrčak ID:
93438
URI
Publication date:
22.12.2010.
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