Título: Achieving an appropriate balance between precision, support, and comprehensibility in the evolution of classification rules
Autores: Carreño, Emiliano
Leguizamón, Guillermo
Fecha: 2012-10-18
2006-10
2006-10
Publicador: Unversidad Nacional de La Plata
Fuente:

Tipo: Objeto de conferencia
Objeto de conferencia
Tema: classification rules
ranking
genetic programming
comprehensible knowledge
Data mining
Ciencias Informáticas
Descripción: This article proposes a method for achieving an appropriate balance between the parameters of support, precision, and simplicity during the evolution of classification rules by means of genetic programming. The method includes an adaptive procedure in order to achieve such balance. This work lies within the data mining context, more precisely, it focuses on the extraction of comprehensible knowledge where the approach introduced plays a predominant role. Experimental results demonstrate the advantages of using the proposed method
Idioma: Inglés