An instance selection algorithm based on contribution

Ning ZHANG; Xi-Zhao WANG; Tao XIAO

doi:10.1109/ICMLC.2008.4620536

An instance selection algorithm based on contribution

Ning ZHANG, Xi-Zhao WANG, Tao XIAO

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

1 Citation (Scopus)

Abstract

This paper presents an approach to instance selection for the nearest neighbor rule which aims to obtain a condensed set with high condensing rate and prediction accuracy. By making an improvement on MCS algorithm and allowing certain error rate on the training set, a condensed set with high condensing rate and satisfying prediction accuracy is obtained. The condensed set is order-independent of the training instances and insensitive to noise. Comparative experiments have been conducted on real data sets, and the results show its superiority to MCS and FCNN in terms of condensing rate and prediction accuracy.

Original language	English
Title of host publication	Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC
Publisher	IEEE
Pages	919-923
Number of pages	5
ISBN (Print)	9781424420957
DOIs	https://doi.org/10.1109/ICMLC.2008.4620536
Publication status	Published - 2008
Externally published	Yes
Event	7th International Conference on Machine Learning and Cybernetics, ICMLC - Kunming, China Duration: 12 Jul 2008 → 15 Jul 2008

Publication series

Name	International Conference on Machine Learning and Cybernetics (ICMLC)
Publisher	IEEE
ISSN (Print)	2160-133X
ISSN (Electronic)	2160-1348

Conference

Conference	7th International Conference on Machine Learning and Cybernetics, ICMLC
Country/Territory	China
City	Kunming
Period	12/07/08 → 15/07/08

Bibliographical note

This research is supported by the Natural Science Foundation of Hebei Province (F2008000635), by the key project foundation of applied fundamental research of Hebei Province (08963522D), by the plan of 100 excellent innovative scientists of the first group in Education Department of Hebei Province, and by the Scientific Research Foundation of Hebei Province (06213548).

Keywords

Condensed set
FCNN
Instance selection
MCS
Nearest neighbor rule

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10.1109/ICMLC.2008.4620536

Cite this

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title = "An instance selection algorithm based on contribution",

abstract = "This paper presents an approach to instance selection for the nearest neighbor rule which aims to obtain a condensed set with high condensing rate and prediction accuracy. By making an improvement on MCS algorithm and allowing certain error rate on the training set, a condensed set with high condensing rate and satisfying prediction accuracy is obtained. The condensed set is order-independent of the training instances and insensitive to noise. Comparative experiments have been conducted on real data sets, and the results show its superiority to MCS and FCNN in terms of condensing rate and prediction accuracy.",

keywords = "Condensed set, FCNN, Instance selection, MCS, Nearest neighbor rule",

author = "Ning ZHANG and Xi-Zhao WANG and Tao XIAO",

note = "This research is supported by the Natural Science Foundation of Hebei Province (F2008000635), by the key project foundation of applied fundamental research of Hebei Province (08963522D), by the plan of 100 excellent innovative scientists of the first group in Education Department of Hebei Province, and by the Scientific Research Foundation of Hebei Province (06213548). ; 7th International Conference on Machine Learning and Cybernetics, ICMLC ; Conference date: 12-07-2008 Through 15-07-2008",

year = "2008",

doi = "10.1109/ICMLC.2008.4620536",

language = "English",

isbn = "9781424420957",

series = "International Conference on Machine Learning and Cybernetics (ICMLC)",

publisher = "IEEE",

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booktitle = "Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC",

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ZHANG, N, WANG, X-Z & XIAO, T 2008, An instance selection algorithm based on contribution. in Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC., 4620536, International Conference on Machine Learning and Cybernetics (ICMLC), IEEE, pp. 919-923, 7th International Conference on Machine Learning and Cybernetics, ICMLC, Kunming, China, 12/07/08. https://doi.org/10.1109/ICMLC.2008.4620536

An instance selection algorithm based on contribution. / ZHANG, Ning; WANG, Xi-Zhao; XIAO, Tao.
Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC. IEEE, 2008. p. 919-923 4620536 (International Conference on Machine Learning and Cybernetics (ICMLC)).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

TY - GEN

T1 - An instance selection algorithm based on contribution

AU - ZHANG, Ning

AU - WANG, Xi-Zhao

AU - XIAO, Tao

N1 - This research is supported by the Natural Science Foundation of Hebei Province (F2008000635), by the key project foundation of applied fundamental research of Hebei Province (08963522D), by the plan of 100 excellent innovative scientists of the first group in Education Department of Hebei Province, and by the Scientific Research Foundation of Hebei Province (06213548).

PY - 2008

Y1 - 2008

N2 - This paper presents an approach to instance selection for the nearest neighbor rule which aims to obtain a condensed set with high condensing rate and prediction accuracy. By making an improvement on MCS algorithm and allowing certain error rate on the training set, a condensed set with high condensing rate and satisfying prediction accuracy is obtained. The condensed set is order-independent of the training instances and insensitive to noise. Comparative experiments have been conducted on real data sets, and the results show its superiority to MCS and FCNN in terms of condensing rate and prediction accuracy.

AB - This paper presents an approach to instance selection for the nearest neighbor rule which aims to obtain a condensed set with high condensing rate and prediction accuracy. By making an improvement on MCS algorithm and allowing certain error rate on the training set, a condensed set with high condensing rate and satisfying prediction accuracy is obtained. The condensed set is order-independent of the training instances and insensitive to noise. Comparative experiments have been conducted on real data sets, and the results show its superiority to MCS and FCNN in terms of condensing rate and prediction accuracy.

KW - Condensed set

KW - FCNN

KW - Instance selection

KW - MCS

KW - Nearest neighbor rule

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DO - 10.1109/ICMLC.2008.4620536

M3 - Conference paper (refereed)

AN - SCOPUS:57849083154

SN - 9781424420957

T3 - International Conference on Machine Learning and Cybernetics (ICMLC)

SP - 919

EP - 923

BT - Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC

PB - IEEE

T2 - 7th International Conference on Machine Learning and Cybernetics, ICMLC

Y2 - 12 July 2008 through 15 July 2008

ER -

An instance selection algorithm based on contribution

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Keywords

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