Proceedings of the 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016)

An Improved AdaBoost-SVM Model Based on Sample Weights and Sampling Equilibrium

Authors
Hongchen Guo, Junbang Ma, Zhiqiang Li
Corresponding Author
Hongchen Guo
Available Online December 2016.
DOI
https://doi.org/10.2991/iceeecs-16.2016.9How to use a DOI?
Keywords
AdaBoost,SVM, Sample Weights, Sampling Equilibrium
Abstract
The existing model which combines AdaBoost and SVM has poor performance when dealing with the imbalance dataset in multi-label classification. To deal with this problem, we proposed a new model SAB-WSVM. In our model, we modified AdaBoost original sampling methods in order to make it more balanced and more informative. Also we combined the sample weights of SVM and weights of AdaBoost to make SVM pay more attention to the samples which are difficult to be classified.In the experiment, we test it with two datasets. The results show that our model has better performance in the unbalanced multi-label datasets.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016)
Part of series
Advances in Computer Science Research
Publication Date
December 2016
ISBN
978-94-6252-265-7
ISSN
2352-538X
DOI
https://doi.org/10.2991/iceeecs-16.2016.9How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Hongchen Guo
AU  - Junbang Ma
AU  - Zhiqiang Li
PY  - 2016/12
DA  - 2016/12
TI  - An Improved AdaBoost-SVM Model Based on Sample Weights and Sampling Equilibrium
BT  - 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/iceeecs-16.2016.9
DO  - https://doi.org/10.2991/iceeecs-16.2016.9
ID  - Guo2016/12
ER  -