%0 Journal Article %1 espejo:2006:AEPIA %A Espejo, Pedro G. %A Hervas, Cesar %A Ventura, Sebastian %A Romero, Cristobal %D 2006 %J Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial %K Clasificacion, Comprensibilidad, Evolution, Grammatical Mineria Programacion algorithms, datos, de genetic genetica gramatical programming, %P 19--30 %T Eleccion de Operadores Logicos para la Induccion de Conocimiento Comprensible %U http://sci2s.ugr.es/keel/pdf/keel/articulo/1cr-1r-2r.pdf %V 29 %X In data mining, the quality of induced knowledge is determined by several features. The focus has been usually placed on accuracy, paying much less attention to comprehensibility. In this paper, we present a rule-based data mining system for classification. Our main goal is the analysis of the trade-off between accuracy and comprehensibility, but we approach comprehensibility from a novel point of view: we are interested in gaining insight into how the use of logical operators affects comprehensibility. In addition, we study the suitability of grammar-based genetic programming for data mining

@article{espejo:2006:AEPIA, abstract = {In data mining, the quality of induced knowledge is determined by several features. The focus has been usually placed on accuracy, paying much less attention to comprehensibility. In this paper, we present a rule-based data mining system for classification. Our main goal is the analysis of the trade-off between accuracy and comprehensibility, but we approach comprehensibility from a novel point of view: we are interested in gaining insight into how the use of logical operators affects comprehensibility. In addition, we study the suitability of grammar-based genetic programming for data mining}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Espejo, Pedro G. and Hervas, Cesar and Ventura, Sebastian and Romero, Cristobal}, biburl = {https://www.bibsonomy.org/bibtex/2153c82f00fec7624fad0bb66af6d599d/brazovayeye}, interhash = {04b0c2200dd4bb586bedb7e7e8e8453d}, intrahash = {153c82f00fec7624fad0bb66af6d599d}, issn = {1137-3601}, journal = {Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial}, keywords = {Clasificacion, Comprensibilidad, Evolution, Grammatical Mineria Programacion algorithms, datos, de genetic genetica gramatical programming,}, note = {Ejemplar dedicado a: Mineria de Datos}, notes = {c AEPIA (http://www.aepia.dsic.upv.es/) In Spanish}, pages = {19--30}, resumen = {Son varias las caracteristicas que determinan la calidad del conocimiento obtenido en el proceso de mineria de datos. De estas caracteristicas, a la que mas atencion se ha dedicado tradicionalmente ha sido la precision, relegandose a un segundo plano la comprensibilidad. En este trabajo desarrollamos un sistema de mineria de datos orientado a la tarea de clasificacion, utilizando reglas como formalismo de representacion. El objetivo principal es analizar el balance entre precision y comprensibilidad, centrandonos en un aspecto de la comprensibilidad poco tratado hasta la fecha: el que viene determinado por la eleccion de los operadores logicos que pueden aparecer en el antecedente de las reglas. El sistema de mineria desarrollado se basa en la programacion genetica gramatical, ya que otro objetivo de nuestro trabajo es estudiar la utilidad de esta tecnica evolutiva para llevar a cabo tareas de mineria.}, size = {12 pages}, timestamp = {2008-06-19T17:39:18.000+0200}, title = {Eleccion de Operadores Logicos para la Induccion de Conocimiento Comprensible}, url = {http://sci2s.ugr.es/keel/pdf/keel/articulo/1cr-1r-2r.pdf}, volume = 29, year = 2006 }