Multi-Classifier Based on Hard Instances- New Method for Prediction of Human Immunodeficiency Virus Drug Resistance

Isis      Bonet; Joel      Arencibia; Mario      Pupo; Abdel      Rodriguez; Maria M.      Garcia; Ricardo      Grau

Abstract

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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