Enrollment Management Model: Artificial Neural Networks versus Logistic Regression

This paper presents an enrollment management model by applying artificial neural network (ANN). The aim of the research, which has been presented in this paper, is to show that ANNs are more successful in predicting than the classical statistical method - regression analysis (logistic regression). B...

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Bibliographic Details
Published in:Applied artificial intelligence 2018-04, Vol.32 (2), p.153-164
Main Authors: Gerasimovic, Milica, Bugaric, Ugljesa
Format: Article
Language:English
Subjects:
Artificial neural networks
Decision making
Dependent variables
Independent variables
Mathematical models
Neural networks
Regression analysis
Statistical analysis
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Summary:This paper presents an enrollment management model by applying artificial neural network (ANN). The aim of the research, which has been presented in this paper, is to show that ANNs are more successful in predicting than the classical statistical method - regression analysis (logistic regression). Both predictive models, no matter whether they are based on ANNs or logistic regression, offer satisfactory predictive results, and they can offer support in the decision-making process. However, the model based on neural networks shows certain advantages. ANNs demand understanding of functional connection between independent and dependent variables in order to evaluate the model. Also, they adapt easily to related independent variables, without the appearance of the problem of multicollinearity. In contrast to logistic regression, neural networks can recognize the appearance of nonlinearity and interactions in input data, and they can react on time.
ISSN:0883-9514
1087-6545
DOI:10.1080/08839514.2018.1448146