Using a genetic algorithm for editing k-nearest neighbor classifiers


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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. © Springer-Verlag Berlin Heidelberg 2007.
Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning : IDEAL 2007 : 8th International Conference, Birmingham, UK, December 16-19, 2007, Proceedings
EditorsHujun YIN, Peter TINO, Emilio CORCHADO, Will BYRNE, Xin YAO
PublisherSpringer Berlin Heidelberg
Number of pages10
ISBN (Electronic)9783540772262
ISBN (Print)9783540772255
Publication statusPublished - 6 Dec 2007
Externally publishedYes
Event8th International Conference Intelligent Data Engineering and Automated Learning, IDEAL 2007 - Birmingham, United Kingdom
Duration: 16 Dec 200719 Dec 2007

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin, Heidelberg
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference8th International Conference Intelligent Data Engineering and Automated Learning, IDEAL 2007
Country/TerritoryUnited Kingdom


  • Objective Function
  • Genetic Algorithm
  • Mean Square Error
  • Training Pattern
  • Neighbor Rule


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