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Author: search.filters.author.Udomboso, C. G.Subject: search.filters.subject.Model selection criteria

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    Modeling students’ academic performance using artificial neural network
    (Federal University, Ndufu-Alike Ikwo (FUNAI), Nigeria, 2016) Asogwa, O. C.; Udomboso, C. G.
    Artificial Neural Network has been discovered as a better alternative to traditional models and that is why a model based on the Multilayer Perceptron algorithm was developed in this study. The appropriate number of hidden neurons that best modeled the academic performance of students was determined by the developed Network algorithm. Test data evaluation showed that Network Architecture 17-80 -1 was chosen among the numerous developed network architectures because of its model performances. The chosen network architecture gave the minimum value of Mean Square Error (MSE = 0.0718), minimum value of Network Information Criteria (NIC = 0.0743), maximum value of R- Square (R2=0.8975) and maximum value of Adjusted Network Information Criteria (ANIC= 0.8931). It was equally observed that there were patterns in the movement of hidden neurons against the model evaluation criteria. As the number of the hidden neurons appreciates the value of both MSE and NIC decreases down the plot, while that of ^-Square and ^MCvalues appreciate down the plot. The network was able to model the research problem with acceptable values judging from the model checking criteria considered in this work. Also the order of contribution of the predictor variables to the model was determined.
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    Comparative analysis of rainfall prediction using statistical neural network and classical linear regression model
    (Medwell Journals, 2011) Udomboso, C. G.; Amahia., G. N.
    Different types of models have been used in modeling rainfall. Since 1990s however, interest has shifted from traditional models to ANN in rainfall modeling. Many researchers found out that the ANN performed better than such traditional models. In this study, we compared a traditional linear model and ANN in the modeling of rainfall in Ibadan, Nigeria. Ibadan is a city in West Africa, located in the tropical rainforest zone, using the data obtained from the Nigeria Meteorological (NIMET) station. Three variables were considered in this study rainfall, temperature and humidity. In selecting between the two models, we concentrated on the choice of adjusted R2 (R-2 ), Akaike Information Criterion (AIC) and Schwarz Information Criterion (SIC). Though, the MSE and R2 were also used, it was concluded from results that MSE is not a good choice for model selection. This is due to the nature of the rainfall data (which has wide variations). It was found that the Statistical Neural Network (SNN), generally performed better than the traditional (OLS).