@misc{10481/64952,
year = {2012},
url = {http://hdl.handle.net/10481/64952},
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.},
organization = {Spanish Government

TIN2011-28488},
organization = {Excellence Regional Project 	
TIC-6858},
organization = {Spanish Government},
publisher = {IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC},
keywords = {Cooperative coevolution},
keywords = {Feature weighting (FW)},
keywords = {Instance selection (IS)},
keywords = {Instance weighting (IW)},
keywords = {Nearest neighbor rule},
title = {Integrating Instance Selection, Instance Weighting, and Feature Weighting for Nearest Neighbor Classifiers by Coevolutionary Algorithms},
doi = {10.1109/TSMCB.2012.2191953},
author = {Derrac Rus, Joaquín and Triguero, Isaac and García, Salvador and Herrera Triguero, Francisco},
}