caa a22 4500 463251326 CHVBK 20180405153341.0 cr unu---uuuuu 170326e20070101xx s 000 0 eng 10.1007/s10791-006-9012-6 doi (NATIONALLICENCE)springer-10.1007/s10791-006-9012-6 Evolutionary learning of document categories [Elektronische Daten] [J. Serrano, M. Castillo] This 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. Springer Science + Business Media, LLC., 2006 Genetic algorithms nationallicence Text categorization nationallicence Distance-based methods nationallicence Serrano J. Instituto de Automática Industrial, CSIC, Madrid, Spain aut Castillo M. Instituto de Automática Industrial, CSIC, Madrid, Spain aut Information Retrieval Kluwer Academic Publishers 10/1(2007-01-01), 69-83 1386-4564 10:1<69 2007 10 10791 https://doi.org/10.1007/s10791-006-9012-6 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10791-006-9012-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Serrano J. Instituto de Automática Industrial, CSIC, Madrid, Spain aut NATIONALLICENCE 700 1- Castillo M. Instituto de Automática Industrial, CSIC, Madrid, Spain aut NATIONALLICENCE 773 0- Information Retrieval Kluwer Academic Publishers 10/1(2007-01-01), 69-83 1386-4564 10:1<69 2007 10 10791 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer