There are several classification problems, which are difficult to solve using a single classifier because of the
complexity of the decision boundary. Whereas a wide variety of multiple classifier systems have been built with the purpose
of improving the recognition process, there is no universal method performing the best. This paper provides a review
of different multi-classifiers and some application of them. Also it is shown a novel model of combining classifiers and its
application to predicting human immunodeficiency virus drug resistance from genotype. The proposal is based on the use
of different classifier models. It clusters the dataset considering the performance of the base classifiers. The system learns
how to decide from the groups, by using a meta-classifier, which are the best classifiers for a given pattern. The proposed
model is compared with well-known classifier ensembles and individual classifiers as well resulting the novel model in
similar or even better performance.
Keywords: Classification, Ensemble classifiers, HIV prediction, Machine learning, Multi-classifiers.
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