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Comparing generalized additive neural networks with multilayer perceptrons / Johannes Christiaan Goosen

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dc.contributor.author Goosen, Johannes Christiaan en_US
dc.date.accessioned 2012-02-17T08:21:56Z
dc.date.available 2012-02-17T08:21:56Z
dc.date.issued 2011 en_US
dc.identifier.uri http://hdl.handle.net/10394/5552
dc.description Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2011.
dc.description.abstract In this dissertation, generalized additive neural networks (GANNs) and multilayer perceptrons (MLPs) are studied and compared as prediction techniques. MLPs are the most widely used type of artificial neural network (ANN), but are considered black boxes with regard to interpretability. There is currently no simple a priori method to determine the number of hidden neurons in each of the hidden layers of ANNs. Guidelines exist that are either heuristic or based on simulations that are derived from limited experiments. A modified version of the neural network construction with cross–validation samples (N2C2S) algorithm is therefore implemented and utilized to construct good MLP models. This algorithm enables the comparison with GANN models. GANNs are a relatively new type of ANN, based on the generalized additive model. The architecture of a GANN is less complex compared to MLPs and results can be interpreted with a graphical method, called the partial residual plot. A GANN consists of an input layer where each of the input nodes has its own MLP with one hidden layer. Originally, GANNs were constructed by interpreting partial residual plots. This method is time consuming and subjective, which may lead to the creation of suboptimal models. Consequently, an automated construction algorithm for GANNs was created and implemented in the SAS R statistical language. This system was called AutoGANN and is used to create good GANN models. A number of experiments are conducted on five publicly available data sets to gain insight into the similarities and differences between GANN and MLP models. The data sets include regression and classification tasks. In–sample model selection with the SBC model selection criterion and out–of–sample model selection with the average validation error as model selection criterion are performed. The models created are compared in terms of predictive accuracy, model complexity, comprehensibility, ease of construction and utility. The results show that the choice of model is highly dependent on the problem, as no single model always outperforms the other in terms of predictive accuracy. GANNs may be suggested for problems where interpretability of the results is important. The time taken to construct good MLP models by the modified N2C2S algorithm may be shorter than the time to build good GANN models by the automated construction algorithm en_US
dc.publisher North-West University
dc.subject ANN en_US
dc.subject Artificial neural network en_US
dc.subject AutoGANN en_US
dc.subject GANN en_US
dc.subject Generalized additive neural network en_US
dc.subject Insample model selection en_US
dc.subject MLP en_US
dc.subject Multilayer perceptron en_US
dc.subject N2C2S algorithm en_US
dc.subject Out-of-sample model selection en_US
dc.subject Prediction en_US
dc.subject Predictive modelling en_US
dc.subject SBC en_US
dc.subject Schwarz information criterion en_US
dc.subject KNN en_US
dc.subject Kunsmatige neurale netwerk en_US
dc.subject Veralgemeende additiewe neurale netwerk en_US
dc.subject VANN en_US
dc.subject In-steekproefmodel-seleksie en_US
dc.subject Multilaag perseptron en_US
dc.subject N2K2S-algoritme en_US
dc.subject Buite-steekproefmodel-seleksie en_US
dc.subject Voorspelling en_US
dc.subject Voorspellingsmodellering en_US
dc.subject Schwarz-inligtingskriterium en_US
dc.title Comparing generalized additive neural networks with multilayer perceptrons / Johannes Christiaan Goosen en_US
dc.type Thesis en_US
dc.description.thesistype Masters en_US


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    This collection contains the original digitized versions of research conducted at the North-West University (Potchefstroom Campus)

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