Using a genetic algorithm for editing k-nearest neighbor classifiers

Authors:
R. Gil-Pita;X. Yao
Affiliations:
Teoría de la Señal y Comunicaciones, Universidad de Alcalá, Madrid, Spain;Computer Sciences Department, University of Birmingham, Birmingham, UK and Nature Inspired Computation and Applications Laboratory, University of Science and Technology of China, Hefei, Anhui, P.R ...
Venue:
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Year:
2007

Citing 6
Cited 5

Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm

Pattern Recognition Letters
Data characterization for effective prototype selection

IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Another move toward the minimum consistent subset: a tabu searchapproach to the condensed nearest neighbor rule

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new evolutionary system for evolving artificial neural networks

IEEE Transactions on Neural Networks
A constructive algorithm for training cooperative neural network ensembles

IEEE Transactions on Neural Networks

A GA-based flexible learning algorithm with error tolerance for digital binary neural networks

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
An efficient design of a nearest neighbor classifier for various-scale problems

Pattern Recognition Letters
Edited AdaBoost by weighted kNN

Neurocomputing
Representative prototype sets for data characterization and classification

ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
Identifying predictive hubs to condense the training set of $$k$$-nearest neighbour classifiers

Computational Statistics

Quantified Score

Hi-index	0.00

Visualization

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.