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

Author
Velez-Langs,Oswaldo

Full text
https://scielo.conicyt.cl/scielo.php?script=sci_arttext&pid=S0718-33052014000100013
Abstract
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).
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Discipline
Artes, Arquitectura y UrbanismoCiencias Agrarias, Forestales y VeterinariasCiencias Exactas y NaturalesCiencias SocialesDerechoEconomía y AdministraciónFilosofía y HumanidadesIngenieríaMedicinaMultidisciplinarias
Institutions
Universidad de ChileUniversidad Católica de ChileUniversidad de Santiago de ChileUniversidad de ConcepciónUniversidad Austral de ChileUniversidad Católica de ValparaísoUniversidad del Bio BioUniversidad de ValparaísoUniversidad Católica del Nortemore

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