An improved support vector machine with soft decision-making boundary

Boyang Li; Jinglu Hu; Kotaro Hirasawa

An improved support vector machine with soft decision-making boundary

Boyang Li, Jinglu Hu, Kotaro Hirasawa

Graduate School of Information, Production and Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

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 SVMwith soft decisionmaking boundary increases classification accuracy by reducing the effects of data unbalance and noises in the realworld data. Simulation results show the effectiveness of the proposed approach.

Original language	English
Title of host publication	Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2008
Pages	40-45
Number of pages	6
Publication status	Published - 2008 Dec 1
Event	IASTED International Conference on Artificial Intelligence and Applications, AIA 2008 - Innsbruck, Austria Duration: 2008 Feb 13 → 2008 Feb 15

Publication series

Name	Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2008

Conference

Conference	IASTED International Conference on Artificial Intelligence and Applications, AIA 2008
Country/Territory	Austria
City	Innsbruck
Period	08/2/13 → 08/2/15

Keywords

Real-world data classification
SVM
Soft decision-making boundary
Unbalanced data

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications

Cite this

An improved support vector machine with soft decision-making boundary. / Li, Boyang; Hu, Jinglu; Hirasawa, Kotaro.

Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2008. 2008. p. 40-45 (Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2008).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Li, B, Hu, J & Hirasawa, K 2008, An improved support vector machine with soft decision-making boundary. in Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2008. Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2008, pp. 40-45, IASTED International Conference on Artificial Intelligence and Applications, AIA 2008, Innsbruck, Austria, 08/2/13.

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AB - 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 SVMwith soft decisionmaking boundary increases classification accuracy by reducing the effects of data unbalance and noises in the realworld data. Simulation results show the effectiveness of the proposed approach.

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An improved support vector machine with soft decision-making boundary

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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