An Improved AdaBoost-SVM Model Based on Sample Weights and Sampling Equilibrium
Hongchen Guo, Junbang Ma, Zhiqiang Li
Available Online December 2016.
- https://doi.org/10.2991/iceeecs-16.2016.9How to use a DOI?
- AdaBoost,SVM, Sample Weights, Sampling Equilibrium
- 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.
- 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 -