Feature reduction using a RBF network for classification of learning styles in first year engineering students

When having a large number of variables in the input of an Artificial Neural Network (ANN), there are different problems in the design, structure and performance of the network itself. Feature reduction is the technique of selecting a subset of 'relevant' features for building robust learn...

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Autor principal:	Velez-Langs,Oswaldo
Lenguaje:	English
Publicado:	Universidad de Tarapacá. 2014
Materias:	Feature selection interface adaptation principal component analysis radial basis function neural networks user modeling
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052014000100013
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spelling	oai:scielo:S0718-330520140001000132014-09-02Feature reduction using a RBF network for classification of learning styles in first year engineering studentsVelez-Langs,Oswaldo Feature selection interface adaptation principal component analysis radial basis function neural networks user modeling When having a large number of variables in the input of an Artificial Neural Network (ANN), there are different problems in the design, structure and performance of the network itself. Feature reduction is the technique of selecting a subset of 'relevant' features for building robust learning models as in an artificial neural network. In this paper, the well-known Principal Component Analysis (PCA) approach is applied in order to tackle this phenomenon in the design of an ANN with Radial Basis Functions (RBF) to be applied to classify users according to predefined learning styles. The model is developed upon a data set built from answers provided by 183 users of a computer interface to a series of 80 questions (that correspond to characteristics related to users learning style), associated to one of four (4) possible classifications/styles. This data set, without pre processing, is initially used for training an ANN with a Radial Basis Function type (RBF). Then, the Principal Component Analysis (PCA) is used for preprocessing the data set, the quantity of dimensions is reduced (80 measured characteristics) which are the input to the ANN. The main objective is to see the relevance that an ANN could have as classifier element in the User Adaptive Systems (UAS).info:eu-repo/semantics/openAccessUniversidad de Tarapacá.Ingeniare. Revista chilena de ingeniería v.22 n.1 20142014-01-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052014000100013en10.4067/S0718-33052014000100013
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Feature selection interface adaptation principal component analysis radial basis function neural networks user modeling
spellingShingle	Feature selection interface adaptation principal component analysis radial basis function neural networks user modeling Velez-Langs,Oswaldo Feature reduction using a RBF network for classification of learning styles in first year engineering students
description	When having a large number of variables in the input of an Artificial Neural Network (ANN), there are different problems in the design, structure and performance of the network itself. Feature reduction is the technique of selecting a subset of 'relevant' features for building robust learning models as in an artificial neural network. In this paper, the well-known Principal Component Analysis (PCA) approach is applied in order to tackle this phenomenon in the design of an ANN with Radial Basis Functions (RBF) to be applied to classify users according to predefined learning styles. The model is developed upon a data set built from answers provided by 183 users of a computer interface to a series of 80 questions (that correspond to characteristics related to users learning style), associated to one of four (4) possible classifications/styles. This data set, without pre processing, is initially used for training an ANN with a Radial Basis Function type (RBF). Then, the Principal Component Analysis (PCA) is used for preprocessing the data set, the quantity of dimensions is reduced (80 measured characteristics) which are the input to the ANN. The main objective is to see the relevance that an ANN could have as classifier element in the User Adaptive Systems (UAS).
author	Velez-Langs,Oswaldo
author_facet	Velez-Langs,Oswaldo
author_sort	Velez-Langs,Oswaldo
title	Feature reduction using a RBF network for classification of learning styles in first year engineering students
title_short	Feature reduction using a RBF network for classification of learning styles in first year engineering students
title_full	Feature reduction using a RBF network for classification of learning styles in first year engineering students
title_fullStr	Feature reduction using a RBF network for classification of learning styles in first year engineering students
title_full_unstemmed	Feature reduction using a RBF network for classification of learning styles in first year engineering students
title_sort	feature reduction using a rbf network for classification of learning styles in first year engineering students
publisher	Universidad de Tarapacá.
publishDate	2014
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052014000100013
work_keys_str_mv	AT velezlangsoswaldo featurereductionusingarbfnetworkforclassificationoflearningstylesinfirstyearengineeringstudents
_version_	1714203411421855744

Feature reduction using a RBF network for classification of learning styles in first year engineering students

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