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Título

Evolutionary learning of document categories

AutorSerrano Moreno, José Ignacio; Castillo Sobrino, María Dolores del
Palabras claveGenetic algorithms
Text categorization
Distance-based methods
Fecha de publicaciónago-2006
EditorSpringer
CitaciónInformation Retrieval (2007) 10:69-83
ResumenThis paper deals with a supervised learning method devoted to producing categorization models of text documents. The goal of the method is to use a suitable numerical measurement of example similarity to find centroids describing different categories of examples. The centroids are not abstract or statistical models, but rather consist of bits of examples. The centroid-learning method is based on a Genetic Algorithm for Texts (GAT). The categorization system using this genetic algorithm infers a model by applying the genetic algorithm to each set of preclassified documents belonging to a category. The models thus obtained are the category centroids that are used to predict the category of a test document. The experimental results validate the utility of this approach for classifying incoming documents.
Versión del editorhttp://www.springerlink.com/content/yg1147w07864267q/
URIhttp://hdl.handle.net/10261/21701
DOIDOI 10.1007/s10791-006-9012-6
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