Mejoramiento evolutivo de parámetros en un clasificador asociativo (Evolutive Improvement of Parameters in an Associative Classifier)

Antonio Ramírez (, Itzamá López (, Yenny Villuendas (, Cornelio Yáńez (

1Centro de Investigación en Computación del Instituto Politécnico Nacional
2Centro de Innovación y Desarrollo Tecnológico en Cómputo del Instituto Politécnico Nacional
3Universidad de Ciego de Ávila

This paper appears in: Revista IEEE América Latina

Publication Date: May 2015
Volume: 13,   Issue: 5 
ISSN: 1548-0992

This paper presents an effective method to improve some of the parameters in an associative classifier, thus increasing its performance. This is accomplished using the simplicity and symmetry of the differential evolution metaheuristic. When modifying some parameters contained in the Gamma associative classifier, which is a novel associative model for pattern classification, this model have been found to be more efficient in the correct discrimination of objects; experimental results show that applying evolutionary algorithms models the desired efficiency and robustness of the classifier model is achieved. In this first approach, improving the Gamma associative classifier is achieved by applying the differential evolution algorithm.

Index Terms:
pattern classification, metaheuristics, Gamma associative classifier, differential evolution   

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