A New Features Selection model: Least Squares Support Vector Machine with Mixture of Kernel
- 10.2991/icaemt-15.2015.127How to use a DOI?
- data classification;LS-SVM-MK;mixture kernel;SVM;LS-SVM.
In this paper, a least squares support vector machine with mixture kernel (LS-SVM-MK) is proposed to solve the problem of the traditional LS-SVM model, such as the loss of sparseness and robustness. Thus that will result in slow testing speed and poor generalization performance. The revision model LS-SVM-MK is equivalent to solve a linear equation set with deficient rank just like the over complete problem in independent component analysis. A minimum of 1-penalty based object function is chosen to get the sparse and robust solution. Some UCI datasets are used to demonstrate the effectiveness of this model. The experimental results show that LS-SVM-MK can obtain a small number of features and improve the generalization ability of LS-SVM.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Liwei Wei AU - Wenwu Li AU - Qiang Xiao PY - 2015/08 DA - 2015/08 TI - A New Features Selection model: Least Squares Support Vector Machine with Mixture of Kernel BT - Proceedings of the 2015 International Conference on Advanced Engineering Materials and Technology PB - Atlantis Press SP - 665 EP - 670 SN - 2352-5401 UR - https://doi.org/10.2991/icaemt-15.2015.127 DO - 10.2991/icaemt-15.2015.127 ID - Wei2015/08 ER -