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 |
| Event | IASTED International Conference on Artificial Intelligence and Applications, AIA 2008 - Innsbruck Duration: 2008 Feb 13 → 2008 Feb 15 |
Other
| Other | IASTED International Conference on Artificial Intelligence and Applications, AIA 2008 |
|---|---|
| City | Innsbruck |
| Period | 08/2/13 → 08/2/15 |
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Keywords
- Real-world data classification
- Soft decision-making boundary
- SVM
- 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; Furuzuki, Takayuki; Hirasawa, Kotaro.
Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2008. 2008. p. 40-45.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - An improved support vector machine with soft decision-making boundary
AU - Li, Boyang
AU - Furuzuki, Takayuki
AU - Hirasawa, Kotaro
PY - 2008
Y1 - 2008
N2 - 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.
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.
KW - Real-world data classification
KW - Soft decision-making boundary
KW - SVM
KW - Unbalanced data
UR - http://www.scopus.com/inward/record.url?scp=62849112519&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=62849112519&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9780889867093
SP - 40
EP - 45
BT - Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2008
ER -