Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm

Shinn-Ying Ho, Chia Cheng Liu, Soundy Liu, Jun Wen Jou

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations

Abstract

The goal of designing an optimal nearest-neighbor classifier is to maximize the classification accuracy while minimizing the sizes of both the reference and feature sets. A novel intelligent genetic algorithm (IGA), which is superior to conventional genetic algorithms (GAs) in solving large parameter optimization problems, is used to effectively achieve this goal. It is shown empirically that the IGA-designed classifier outperforms existing GA-based and non-GA-based classifiers in terms of classification accuracy and the total number of parameters of the reduced sets.

Original languageEnglish
Pages594-599
Number of pages6
DOIs
StatePublished - 1 Jan 2002
Event2002 Congress on Evolutionary Computation, CEC 2002 - Honolulu, HI, United States
Duration: 12 May 200217 May 2002

Conference

Conference2002 Congress on Evolutionary Computation, CEC 2002
CountryUnited States
CityHonolulu, HI
Period12/05/0217/05/02

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