@inproceedings{9529bf8d65674efc9fc3b16d0d4c1cd7,
title = "Using a genetic algorithm for editing k-nearest neighbor classifiers",
abstract = "The edited k-nearest neighbor consists of the application of the k-nearest neighbor classifier with an edited training set, in order to reduce the classification error rate. This edited training set is a subset of the complete training set in which some of the training patterns are excluded. In recent works, genetic algorithms have been successfully applied to generate edited sets. In this paper we propose three improvements of the edited k-nearest neighbor design using genetic algorithms: the use of a mean square error based objective function the implementation of a clustered crossover, and a fast smart mutation scheme. Results achieved using the breast cancer database and the diabetes database from the UCI machine learning benchmark repository demonstrate the improvement achieved by the joint use of these three proposals. {\textcopyright} Springer-Verlag Berlin Heidelberg 2007.",
keywords = "Objective Function, Genetic Algorithm, Mean Square Error, Training Pattern, Neighbor Rule",
author = "R. GIL-PITA and X. YAO",
year = "2007",
month = dec,
day = "6",
doi = "10.1007/978-3-540-77226-2_114",
language = "English",
isbn = "9783540772255",
series = "Lecture Notes in Computer Science",
publisher = "Springer Berlin Heidelberg",
pages = "1141--1150",
editor = "Hujun YIN and Peter TINO and Emilio CORCHADO and Will BYRNE and Xin YAO",
booktitle = "Intelligent Data Engineering and Automated Learning : IDEAL 2007 : 8th International Conference, Birmingham, UK, December 16-19, 2007, Proceedings",
note = "8th International Conference Intelligent Data Engineering and Automated Learning, IDEAL 2007 ; Conference date: 16-12-2007 Through 19-12-2007",
}