Proximal SVM ensemble based on feature selection

Authors:
Xiaoyan Tao;Hongbing Ji;Zhiqiang Ma
Affiliations:
School of Electronic Engineering, Xidian University, Xi'an, China;School of Electronic Engineering, Xidian University, Xi'an, China;Telecommunication Engineering Institute, Air Force Engineering University, Xi'an, China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Year:
2006

Citing 3
Cited 0

Estimating attributes: analysis and extensions of RELIEF

ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Bagging predictors

Machine Learning
Proximal support vector machine classifiers

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

Ensemble is a very popular learning method. Among most of the existing approaches, Bagging is commonly used. However, Bagging is not very effective on the stable learners. Proximal SVM, a variant of SVM, is a stable learner, so Bagging does not work well for PSVM. For this, two new feature selection based PSVM ensemble methods are proposed, i.e. BRFS and BR. Through perturbing both the training set and the input features, component learners with high accuracy as well as high diversity can be obtained. The experimental results on four datasets from UCI demonstrate that the new approaches perform over a single PSVM and Bagging.