Resumen

BONET, Isis; RODRIGUEZ, Abdel; GARCIA, María M.  y  GRAU, Ricardo. Combining Classifiers for Bioinformatics. Comp. y Sist. [online]. 2012, vol.16, n.2, pp.191-201. ISSN 2007-9737.

There are several classification problems in Bioinformatics which are difficult to solve using artificial intelligence techniques because of the diversity of patterns in datasets. In this paper, an ensemble of classifiers is developed to improve the accuracy of classification in bioinformatics datasets. This model is based on the use of different machine learning methods, and it forms clusters to divide the dataset taking into account the performance of the base methods. By means of a meta-classifier, the system learns to decide which classifiers are the best for a given case. In order to compare the new model with some well-known multi-classifiers, eleven international databases are used. It is demonstrated by statistical tests that results of our model are significantly better than those obtained with previous models.

Palabras llave : Model classification; pattern recognition; learning, multi-classifiers.

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