title: Multi-classifier based on hard instances- new method for prediction of human immunodeficiency virus drug resistance.
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.
authors.affiliation: Escuela de Ingeniería de Antioquia, Medellín, Antioquia. Colombia. ibonetc@gmail.com
authors.forename: Isis
authors.initials: I
authors.lastname: Bonet
authors.forename: Joel
authors.initials: J
authors.lastname: Arencibia
authors.forename: Mario
authors.initials: M
authors.lastname: Pupo
authors.forename: Abdel
authors.initials: A
authors.lastname: Rodriguez
authors.forename: Maria M
authors.initials: MM
authors.lastname: García
authors.forename: Ricardo
authors.initials: R
authors.lastname: Grau
mesh_terms: D019380:Anti-HIV Agents / Q000494:pharmacology*; D024882:Drug Resistance, Viral / Q000187:drug effects*; D015658:HIV Infections / Q000188:drug therapy* / Q000821:virology; D015497:HIV-1 / Q000187:drug effects*; D006801:Humans
chemical_list: D019380:Anti-HIV Agents