@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",
}