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 |
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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 Sept 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 |
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Country/Territory | Ireland |
City | Killarney |
Period | 15/7/12 → 15/7/17 |
Keywords
- Accuracy
- Support vector machines
ASJC Scopus subject areas
- Software
- Artificial Intelligence