Enhanced algorithm performance for classification based on hyper surface using Bagging and adaBoost

Qing He; Fu Zhen Zhuang; Xiu Rong Zhao; Zhong Zhi Shi

doi:10.1109/ICMLC.2007.4370775

Enhanced algorithm performance for classification based on hyper surface using Bagging and adaBoost

Qing He^*
, Fu Zhen Zhuang
, Xiu Rong Zhao
, Zhong Zhi Shi

^*Corresponding author for this work

CAS - Institute of Computing Technology
University of Chinese Academy of Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

To improve the generality ability of Hyper Surface Classification (HSC). Bagging and AdaBoost ensemble learning methods are proposed in this paper. HSC is a covering learning algorithm, in which a model of hyper surface is obtained by adaptively dividing the sample space and then the hyper surface is directly used to classify large database based on Jordan Curve Theorem in Topology. Experiments results confirm that Bagging and AdaBoost can improve the generality ability of Hyper Surface Classification (HSC) in general. However, its behavior is subject to the characteristics of Minimal Consistent Subset for a disjoint Cover set (MCSC). Usually the accuracy of Bagging and AdaBoost can not exceed the accuracy predicted by MCSC. So MCSC is the backstage manipulator of generalization ability.

Original language	English
Title of host publication	Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Pages	3624-3629
Number of pages	6
DOIs	https://doi.org/10.1109/ICMLC.2007.4370775
State	Published - 2007
Externally published	Yes
Event	6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 - Hong Kong, China Duration: 19 Aug 2007 → 22 Aug 2007

Publication series

Name	Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Volume	6

Conference

Conference	6th International Conference on Machine Learning and Cybernetics, ICMLC 2007
Country/Territory	China
City	Hong Kong
Period	19/08/07 → 22/08/07

Keywords

AdaBoost
Bagging
Hyper surface cassification
Minimal consistent subset

Access to Document

10.1109/ICMLC.2007.4370775

Cite this

He, Q., Zhuang, F. Z., Zhao, X. R., & Shi, Z. Z. (2007). Enhanced algorithm performance for classification based on hyper surface using Bagging and adaBoost. In Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007 (pp. 3624-3629). Article 4370775 (Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007; Vol. 6). https://doi.org/10.1109/ICMLC.2007.4370775

He, Qing ; Zhuang, Fu Zhen ; Zhao, Xiu Rong et al. / Enhanced algorithm performance for classification based on hyper surface using Bagging and adaBoost. Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007. 2007. pp. 3624-3629 (Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007).

@inproceedings{d63c2411630647ff80a0a5adddbc7e85,

title = "Enhanced algorithm performance for classification based on hyper surface using Bagging and adaBoost",

abstract = "To improve the generality ability of Hyper Surface Classification (HSC). Bagging and AdaBoost ensemble learning methods are proposed in this paper. HSC is a covering learning algorithm, in which a model of hyper surface is obtained by adaptively dividing the sample space and then the hyper surface is directly used to classify large database based on Jordan Curve Theorem in Topology. Experiments results confirm that Bagging and AdaBoost can improve the generality ability of Hyper Surface Classification (HSC) in general. However, its behavior is subject to the characteristics of Minimal Consistent Subset for a disjoint Cover set (MCSC). Usually the accuracy of Bagging and AdaBoost can not exceed the accuracy predicted by MCSC. So MCSC is the backstage manipulator of generalization ability.",

keywords = "AdaBoost, Bagging, Hyper surface cassification, Minimal consistent subset",

author = "Qing He and Zhuang, \{Fu Zhen\} and Zhao, \{Xiu Rong\} and Shi, \{Zhong Zhi\}",

year = "2007",

doi = "10.1109/ICMLC.2007.4370775",

language = "英语",

isbn = "142440973X",

series = "Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007",

pages = "3624--3629",

booktitle = "Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007",

note = "6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 ; Conference date: 19-08-2007 Through 22-08-2007",

}

He, Q, Zhuang, FZ, Zhao, XR & Shi, ZZ 2007, Enhanced algorithm performance for classification based on hyper surface using Bagging and adaBoost. in Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007., 4370775, Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007, vol. 6, pp. 3624-3629, 6th International Conference on Machine Learning and Cybernetics, ICMLC 2007, Hong Kong, China, 19/08/07. https://doi.org/10.1109/ICMLC.2007.4370775

Enhanced algorithm performance for classification based on hyper surface using Bagging and adaBoost. / He, Qing; Zhuang, Fu Zhen; Zhao, Xiu Rong et al.
Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007. 2007. p. 3624-3629 4370775 (Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007; Vol. 6).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Enhanced algorithm performance for classification based on hyper surface using Bagging and adaBoost

AU - He, Qing

AU - Zhuang, Fu Zhen

AU - Zhao, Xiu Rong

AU - Shi, Zhong Zhi

PY - 2007

Y1 - 2007

N2 - To improve the generality ability of Hyper Surface Classification (HSC). Bagging and AdaBoost ensemble learning methods are proposed in this paper. HSC is a covering learning algorithm, in which a model of hyper surface is obtained by adaptively dividing the sample space and then the hyper surface is directly used to classify large database based on Jordan Curve Theorem in Topology. Experiments results confirm that Bagging and AdaBoost can improve the generality ability of Hyper Surface Classification (HSC) in general. However, its behavior is subject to the characteristics of Minimal Consistent Subset for a disjoint Cover set (MCSC). Usually the accuracy of Bagging and AdaBoost can not exceed the accuracy predicted by MCSC. So MCSC is the backstage manipulator of generalization ability.

AB - To improve the generality ability of Hyper Surface Classification (HSC). Bagging and AdaBoost ensemble learning methods are proposed in this paper. HSC is a covering learning algorithm, in which a model of hyper surface is obtained by adaptively dividing the sample space and then the hyper surface is directly used to classify large database based on Jordan Curve Theorem in Topology. Experiments results confirm that Bagging and AdaBoost can improve the generality ability of Hyper Surface Classification (HSC) in general. However, its behavior is subject to the characteristics of Minimal Consistent Subset for a disjoint Cover set (MCSC). Usually the accuracy of Bagging and AdaBoost can not exceed the accuracy predicted by MCSC. So MCSC is the backstage manipulator of generalization ability.

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KW - Hyper surface cassification

KW - Minimal consistent subset

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ER -

He Q, Zhuang FZ, Zhao XR, Shi ZZ. Enhanced algorithm performance for classification based on hyper surface using Bagging and adaBoost. In Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007. 2007. p. 3624-3629. 4370775. (Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007). doi: 10.1109/ICMLC.2007.4370775

Enhanced algorithm performance for classification based on hyper surface using Bagging and adaBoost

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