Dual neural gas based structure ensemble with the bagging technique

Hantao Chen; Xiaodong Zhang; Jia You; Guoqiang Han; Le Li

doi:10.1109/ICMLC.2012.6359570

Dual neural gas based structure ensemble with the bagging technique

Hantao Chen, Xiaodong Zhang, Jia You, Guoqiang Han, Le Li

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

It is widely accepted that cluster ensemble can improve accuracy, stableness and robustness when compared with single cluster approach. As the bagging technique can enhance the prediction accuracy of unstable learning algorithms, and the neural gas algorithm can achieve the structure of datasets, we propose a new structure ensemble framework, named as dual neural gas based structure ensemble with the bagging technique. Experiments on both UCI datasets and synthetic datasets show that tne new framework works well.

Original language	English
Title of host publication	Proceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012
Pages	1400-1405
Number of pages	6
Volume	4
DOIs	https://doi.org/10.1109/ICMLC.2012.6359570
Publication status	Published - 31 Dec 2012
Event	2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012 - Xian, Shaanxi, China Duration: 15 Jul 2012 → 17 Jul 2012

Conference

Conference	2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012
Country/Territory	China
City	Xian, Shaanxi
Period	15/07/12 → 17/07/12

Keywords

Bagging
Neural gas
Normalized mutual information
Purity
Structure ensemble

ASJC Scopus subject areas

Artificial Intelligence
Computational Theory and Mathematics
Computer Networks and Communications
Human-Computer Interaction

More information

10.1109/ICMLC.2012.6359570

Cite this

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title = "Dual neural gas based structure ensemble with the bagging technique",

abstract = "It is widely accepted that cluster ensemble can improve accuracy, stableness and robustness when compared with single cluster approach. As the bagging technique can enhance the prediction accuracy of unstable learning algorithms, and the neural gas algorithm can achieve the structure of datasets, we propose a new structure ensemble framework, named as dual neural gas based structure ensemble with the bagging technique. Experiments on both UCI datasets and synthetic datasets show that tne new framework works well.",

keywords = "Bagging, Neural gas, Normalized mutual information, Purity, Structure ensemble",

author = "Hantao Chen and Xiaodong Zhang and Jia You and Guoqiang Han and Le Li",

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Chen, H, Zhang, X, You, J, Han, G & Li, L 2012, Dual neural gas based structure ensemble with the bagging technique. in Proceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012. vol. 4, 6359570, pp. 1400-1405, 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012, Xian, Shaanxi, China, 15/07/12. https://doi.org/10.1109/ICMLC.2012.6359570

Dual neural gas based structure ensemble with the bagging technique. / Chen, Hantao; Zhang, Xiaodong; You, Jia et al.
Proceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012. Vol. 4 2012. p. 1400-1405 6359570.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Zhang, Xiaodong

AU - You, Jia

AU - Han, Guoqiang

AU - Li, Le

PY - 2012/12/31

Y1 - 2012/12/31

N2 - It is widely accepted that cluster ensemble can improve accuracy, stableness and robustness when compared with single cluster approach. As the bagging technique can enhance the prediction accuracy of unstable learning algorithms, and the neural gas algorithm can achieve the structure of datasets, we propose a new structure ensemble framework, named as dual neural gas based structure ensemble with the bagging technique. Experiments on both UCI datasets and synthetic datasets show that tne new framework works well.

AB - It is widely accepted that cluster ensemble can improve accuracy, stableness and robustness when compared with single cluster approach. As the bagging technique can enhance the prediction accuracy of unstable learning algorithms, and the neural gas algorithm can achieve the structure of datasets, we propose a new structure ensemble framework, named as dual neural gas based structure ensemble with the bagging technique. Experiments on both UCI datasets and synthetic datasets show that tne new framework works well.

KW - Bagging

KW - Neural gas

KW - Normalized mutual information

KW - Purity

KW - Structure ensemble

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DO - 10.1109/ICMLC.2012.6359570

M3 - Conference article published in proceeding or book

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EP - 1405

BT - Proceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012

T2 - 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012

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ER -

Dual neural gas based structure ensemble with the bagging technique

Abstract

Conference

Keywords

ASJC Scopus subject areas

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