| Title: |
Obtaining Bipartitions from Score Vectors for Multi-Label Classification |
| Author(s): |
M. Ioannou, G. Sakkas, G. Tsoumakas, I. Vlahavas.
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| Availability: |
Click here to download the PDF (Acrobat Reader) file (8 pages).
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| Keywords: |
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| Appeared in: |
22nd International Conference on Tools with Artificial Intelligence, 27-29 October 2010., IEEE, Arras, France, 2010.
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| Abstract: |
Multi-label classification is a popular learning task. However, some of the algorithms that learn from multi-label data, can only output a score for each label, so they cannot be readily used in applications that require bipartitions. In addition, several of the recent state-of-the-art multi-label classification algorithms, actually output a score vector primarily and employ
one (sometimes simple) thresholding method in order to be able to output bipartitions. Furthermore, some approaches can naturally output both a score vector and a bipartition, but whether a better bipartition can be obtained through thresholding has not been investigated. This paper contributes a theoretical and empirical comparative study of existing thresholding methods, highlighting their importance for obtaining bipartitions of high quality. |
| See also : |
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This paper has been cited by the following:
| 1 |
Sanden, C., Zhang, J.Z. (2011) Enhancing multi-label music genre classification through ensemble techniques, SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 705-714.
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| 2 |
Xiao-lin Wang, Hai Zhao, Bao-liang Lu (2011) Enhanced K-Nearest Neighbour Algorithm for Large-scale Hierarchical Multi-label Classification, Proc. Joint ECML/PKDD PASCAL Workshop on Large-Scale Hierarchical Classification, pp. 58-67. |
| 3 |
Rubin, T.N., Chambers, A., Smyth, P., Steyvers, M. (2012) Statistical topic models for multi-label document classification, Machine Learning, 88 (1-2), pp. 157-208. |
| 4 |
Ramón Quevedo, J., Luaces, O., Bahamonde, A. (2012) Multilabel classifiers with a probabilistic thresholding strategy, Pattern Recognition, 45 (2), pp. 876-883.
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