A Preliminary Study on the Use of Fuzzy Rough Set Based Feature Selection for Improving Evolutionary Instance Selection Algorithms Derrac, Joaquín Herrera Triguero, Francisco Fuzzy Rough Sets Evolutionary algorithms Instance selection Feature selection Nearest Neighbor Classifier Inteligencia artificial Artificial intelligence 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. 2022-11-11T12:38:37Z 2022-11-11T12:38:37Z 2011 info:eu-repo/semantics/conferenceObject Published version: Derrac, J... [et al.] (2011). A Preliminary Study on the Use of Fuzzy Rough Set Based Feature Selection for Improving Evolutionary Instance Selection Algorithms. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6691. Springer, Berlin, Heidelberg. [https://doi.org/10.1007/978-3-642-21501-8_22] https://hdl.handle.net/10481/77930 eng http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional Springer