Abstract
This paper proposes an improved SVM based multi-label classification method by using relationship among labels. Following a traditional multi-label solution, binary relevance (BR) method is first used to decompose the multi-label classification problem into multiple binary classification sub-problems, each of which is solved by an SVM classifier. By using Platt's sigmoid technique, each SVM classifier gives probability output for the following correction. A probability model is introduced to estimate the relationship among labels. The extracted label relationship is then applied to correct the outputs of SVM classifiers, in which a dynamic weight strategy is further introduced. Numerical experiments on widely used benchmark datasets show that the proposed method can improve the accuracy of multi-label classification when compared with traditional BR method and some other conventional multi-label classification methods.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the International Joint Conference on Neural Networks |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Volume | 2015-September |
| ISBN (Print) | 9781479919604, 9781479919604, 9781479919604, 9781479919604 |
| DOIs | |
| Publication status | Published - 2015 Sep 28 |
| Event | International Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland Duration: 2015 Jul 12 → 2015 Jul 17 |
Other
| Other | International Joint Conference on Neural Networks, IJCNN 2015 |
|---|---|
| Country | Ireland |
| City | Killarney |
| Period | 15/7/12 → 15/7/17 |
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Keywords
- Accuracy
- Support vector machines
ASJC Scopus subject areas
- Software
- Artificial Intelligence
Cite this
Improving SVM based multi-label classification by using label relationship. / Fu, Di; Zhou, Bo; Furuzuki, Takayuki.
Proceedings of the International Joint Conference on Neural Networks. Vol. 2015-September Institute of Electrical and Electronics Engineers Inc., 2015. 7280497.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Improving SVM based multi-label classification by using label relationship
AU - Fu, Di
AU - Zhou, Bo
AU - Furuzuki, Takayuki
PY - 2015/9/28
Y1 - 2015/9/28
N2 - This paper proposes an improved SVM based multi-label classification method by using relationship among labels. Following a traditional multi-label solution, binary relevance (BR) method is first used to decompose the multi-label classification problem into multiple binary classification sub-problems, each of which is solved by an SVM classifier. By using Platt's sigmoid technique, each SVM classifier gives probability output for the following correction. A probability model is introduced to estimate the relationship among labels. The extracted label relationship is then applied to correct the outputs of SVM classifiers, in which a dynamic weight strategy is further introduced. Numerical experiments on widely used benchmark datasets show that the proposed method can improve the accuracy of multi-label classification when compared with traditional BR method and some other conventional multi-label classification methods.
AB - This paper proposes an improved SVM based multi-label classification method by using relationship among labels. Following a traditional multi-label solution, binary relevance (BR) method is first used to decompose the multi-label classification problem into multiple binary classification sub-problems, each of which is solved by an SVM classifier. By using Platt's sigmoid technique, each SVM classifier gives probability output for the following correction. A probability model is introduced to estimate the relationship among labels. The extracted label relationship is then applied to correct the outputs of SVM classifiers, in which a dynamic weight strategy is further introduced. Numerical experiments on widely used benchmark datasets show that the proposed method can improve the accuracy of multi-label classification when compared with traditional BR method and some other conventional multi-label classification methods.
KW - Accuracy
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=84951191702&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84951191702&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2015.7280497
DO - 10.1109/IJCNN.2015.7280497
M3 - Conference contribution
AN - SCOPUS:84951191702
SN - 9781479919604
SN - 9781479919604
SN - 9781479919604
SN - 9781479919604
VL - 2015-September
BT - Proceedings of the International Joint Conference on Neural Networks
PB - Institute of Electrical and Electronics Engineers Inc.
ER -