Nalazite se na CroRIS probnoj okolini. Ovdje evidentirani podaci neće biti pohranjeni u Informacijskom sustavu znanosti RH. Ako je ovo greška, CroRIS produkcijskoj okolini moguće je pristupi putem poveznice www.croris.hr
izvor podataka: crosbi !

Classification of entrepreneurial intentions by neural networks, decision trees and support vector machines (CROSBI ID 170535)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Zekić-Sušac, Marijana ; Pfeifer, Sanja ; Đurđević, Ivana Classification of entrepreneurial intentions by neural networks, decision trees and support vector machines // Croatian operational research review, 1 (2010), 1; 62-70

Podaci o odgovornosti

Zekić-Sušac, Marijana ; Pfeifer, Sanja ; Đurđević, Ivana

engleski

Classification of entrepreneurial intentions by neural networks, decision trees and support vector machines

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.

classification; entrepreneurial intentions; decision trees; neural networks; support vector machines

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

Podaci o izdanju

1 (1)

2010.

62-70

objavljeno

1848-0225

1848-9931

Povezanost rada

Ekonomija, Informacijske i komunikacijske znanosti

Poveznice
Indeksiranost