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

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

doi:10.1109/CEC.2002.1006993

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

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

Institute of Bioinformatics and Systems Biology

Research output: Contribution to conference › Paper › peer-review

3 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 language	English
Pages	594-599
Number of pages	6
DOIs	https://doi.org/10.1109/CEC.2002.1006993
State	Published - 2002
Event	2002 Congress on Evolutionary Computation, CEC 2002 - Honolulu, HI, United States Duration: 12 May 2002 → 17 May 2002

Conference

Conference	2002 Congress on Evolutionary Computation, CEC 2002
Country/Territory	United States
City	Honolulu, HI
Period	12/05/02 → 17/05/02

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10.1109/CEC.2002.1006993

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title = "Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm",

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.",

author = "Shinn-Ying Ho and Liu, {Chia Cheng} and Soundy Liu and Jou, {Jun Wen}",

year = "2002",

doi = "10.1109/CEC.2002.1006993",

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note = "2002 Congress on Evolutionary Computation, CEC 2002 ; Conference date: 12-05-2002 Through 17-05-2002",

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AU - Jou, Jun Wen

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AB - 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.

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DO - 10.1109/CEC.2002.1006993

M3 - Paper

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T2 - 2002 Congress on Evolutionary Computation, CEC 2002

Y2 - 12 May 2002 through 17 May 2002

ER -

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

Abstract

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