An improved support vector machine with soft decision-making boundary

Authors:
Boyang Li;Jinglu Hu;Kotaro Hirasawa
Affiliations:
Waseda University, Kitakyushu, Fukuoka, Japan;Waseda University, Kitakyushu, Fukuoka, Japan;Waseda University, Kitakyushu, Fukuoka, Japan
Venue:
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Year:
2008

Citing 3
Cited 2

An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Efficient Text Classification by Weighted Proximal SVM

ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Fuzzy support vector machines

IEEE Transactions on Neural Networks

An Improved Support Vector Machine for the Classification of Imbalanced Biological Datasets

ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
GSVM: An SVM for handling imbalanced accuracy between classes inbi-classification problems

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes an improved support vector machine (SVM) classifier by introducing a soft decision-making boundary for solving real-world classification problem. The soft decision-making boundary contains two parameters describing the offset and the shape, which are estimated automatically from the distribution of training samples around the boundary via a distribution of belief degree in the decision value domain. The SVM with soft decision-making boundary increases classification accuracy by reducing the effects of data unbalance and noises in the real-world data. Simulation results show the effectiveness of the proposed approach.