title: Integrating instance selection, instance weighting, and feature weighting for nearest neighbor classifiers by coevolutionary algorithms.
abstract: Cooperative coevolution is a successful trend of evolutionary computation which allows us to define partitions of the domain of a given problem, or to integrate several related techniques into one, by the use of evolutionary algorithms. It is possible to apply it to the development of advanced classification methods, which integrate several machine learning techniques into a single proposal. A novel approach integrating instance selection, instance weighting, and feature weighting into the framework of a coevolutionary model is presented in this paper. We compare it with a wide range of evolutionary and nonevolutionary related methods, in order to show the benefits of the employment of coevolution to apply the techniques considered simultaneously. The results obtained, contrasted through nonparametric statistical tests, show that our proposal outperforms other methods in the comparison, thus becoming a suitable tool in the task of enhancing the nearest neighbor classifier.
authors.affiliation: Department of Computer Science and Artificial Intelligence and the Research Center on Information and Communications Technology (CITIC), University of Granada (UGR), 18071 Granada, Spain. jderrac@decsai.ugr.es
authors.forename: Joaquín
authors.initials: J
authors.lastname: Derrac
authors.forename: Isaac
authors.initials: I
authors.lastname: Triguero
authors.forename: Salvador
authors.initials: S
authors.lastname: Garcia
authors.forename: Francisco
authors.initials: F
authors.lastname: Herrera
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003661:Decision Support Techniques; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*