Porras Cerrón, Jaime C.

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    Componentes principales supervisados para clasificación de datos de expresión genética
    (2005) Porras Cerrón, Jaime C.; Acuña Fernández, Edgar; College of Arts and Sciences - Sciences; Quintana, Julio; Lorenzo, Edgardo; Department of Mathematics; Ducoudray, Gladys
    The gene expression data obtained through the technology of microarrays are characterized by its considerably greater amount of features in comparison to the number of observations. The direct use of traditional statistics techniques of supervised classification can give poor results in gene expression data. Therefore before analyzing this type of data is advisable to perform a dimension reduction. The present work combines two types of dimensional reduction techniques: feature selection and feature extraction. In the first step of the proposed method feature selection is applied, and in the second step principal components are formed with the selected features. This technique is called Supervised Principal Components (SPC). Three classifiers are applied to these components and the misclassification error is estimated. Two algorithms of SPC are presented; they essentially, differ in the time in which the feature selection is made. Finally, the results of this new technique are applied to nine gene expression data sets.