Sistemas multiclasificadores y de aprendizaje por capas basados en CIDIM
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Autores: Gonzalo Ramos Jimenez
, José del Campo Ávila , Rafael Morales Bueno
- Localización: Inteligencia artificial: Revista Iberoamericana de Inteligencia Artificial, ISSN-e 1988-3064, ISSN 1137-3601, Vol. 10, Nº. 29, 2006 (Ejemplar dedicado a: Minería de Datos), págs. 49-58
- Idioma: español
- DOI: 10.4114/ia.v10i29.877
- Enlaces
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Resumen
In this paper we present two multiple classifier systems based on CIDIM. We also describe a layered learning approach that we have particularized using CIDIM as the basic classifier. CIDIM (Control of Induction by sample Division Method) is an algorithm that have been developed to induce accurate and small decision trees and to do this, it tries to reduce the overfitting using a local control of induction. The multiple classifier systems that we present (M-CIDIM and E-CIDIM) take advantage of the characteristics of CIDIM, but the approaches that have been developed can be extended to any other algorithm that shares the same characteristics.
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