Ant Colony reduction with modified rules generation for rough classification model
作者： Azuraliza Abu Bakar , Salwani Abdullah , Faizah Patahol Rahman , Abdul Razak Hamdan
DOI: 10.1109/ISDA.2010.5687055
关键词: Statistical classification 、 Ant colony 、 Ant colony optimization algorithms 、 Classifier (UML) 、 Rough set 、 Data mining 、 Pattern recognition 、 Reduct 、 Mathematics 、 Artificial intelligence
摘要: In this paper we propose a rough classification modeling algorithm based on Ant Colony Optimization (ACO) reduction. We used ACO to compute the set reduct and later modified rules generation method is employed generate rules. The simplification of Default Rules Generation Framework (DRGF) in order fit with reduct. performance proposed classifier compared DRGF using genetic experimental results show that ACO-Rough performs better higher accuracy fewer number
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