Enrollment Management Model: Artificial Neural Networks versus Logistic Regression
Само за регистроване кориснике
2018
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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.
Извор:
Applied Artificial Intelligence, 2018, 32, 2, 153-164Издавач:
- Taylor & Francis Inc, Philadelphia
DOI: 10.1080/08839514.2018.1448146
ISSN: 0883-9514
WoS: 000429983100003
Scopus: 2-s2.0-85044220802
Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Gerasimović, Milica AU - Bugarić, Uglješa PY - 2018 UR - https://machinery.mas.bg.ac.rs/handle/123456789/2808 AB - 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. PB - Taylor & Francis Inc, Philadelphia T2 - Applied Artificial Intelligence T1 - Enrollment Management Model: Artificial Neural Networks versus Logistic Regression EP - 164 IS - 2 SP - 153 VL - 32 DO - 10.1080/08839514.2018.1448146 ER -
@article{ author = "Gerasimović, Milica and Bugarić, Uglješa", year = "2018", abstract = "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.", publisher = "Taylor & Francis Inc, Philadelphia", journal = "Applied Artificial Intelligence", title = "Enrollment Management Model: Artificial Neural Networks versus Logistic Regression", pages = "164-153", number = "2", volume = "32", doi = "10.1080/08839514.2018.1448146" }
Gerasimović, M.,& Bugarić, U.. (2018). Enrollment Management Model: Artificial Neural Networks versus Logistic Regression. in Applied Artificial Intelligence Taylor & Francis Inc, Philadelphia., 32(2), 153-164. https://doi.org/10.1080/08839514.2018.1448146
Gerasimović M, Bugarić U. Enrollment Management Model: Artificial Neural Networks versus Logistic Regression. in Applied Artificial Intelligence. 2018;32(2):153-164. doi:10.1080/08839514.2018.1448146 .
Gerasimović, Milica, Bugarić, Uglješa, "Enrollment Management Model: Artificial Neural Networks versus Logistic Regression" in Applied Artificial Intelligence, 32, no. 2 (2018):153-164, https://doi.org/10.1080/08839514.2018.1448146 . .