@misc{10481/77930,
year = {2011},
url = {https://hdl.handle.net/10481/77930},
abstract = {In recent years, the increasing interest in fuzzy rough set
theory has allowed the definition of novel accurate methods for feature
selection. Although their stand-alone application can lead to the construction
of high quality classifiers, they can be improved even more if
other preprocessing techniques, such as instance selection, are considered.
With the aim of enhancing the nearest neighbor classifier, we present
a hybrid algorithm for instance and feature selection, where evolutionary
search in the instances’ space is combined with a fuzzy rough set based
feature selection procedure. The preliminary results, contrasted through
nonparametric statistical tests, suggest that our proposal can improve
greatly the performance of the preprocessing techniques in isolation.},
organization = {Project TIN2008-06681-C06-01},
organization = {Spanish Ministry of Education},
organization = {Research Foundation - Flanders},
publisher = {Springer},
keywords = {Fuzzy Rough Sets},
keywords = {Evolutionary algorithms},
keywords = {Instance selection},
keywords = {Feature selection},
keywords = {Nearest Neighbor Classifier},
keywords = {Inteligencia artificial},
keywords = {Artificial intelligence},
title = {A Preliminary Study on the Use of Fuzzy Rough Set Based Feature Selection for Improving Evolutionary Instance Selection Algorithms},
author = {Derrac, Joaquín and Herrera Triguero, Francisco},
}