| Title: |
An Empirical Study of Lazy Multilabel Classification Algorithms |
| Author(s): |
E. Spyromitros-Xioufis, G. Tsoumakas, I. Vlahavas.
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| Availability: |
Click here to download the PDF (Acrobat Reader) file (6 pages).
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| Keywords: |
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| Appeared in: |
Proc. 5th Hellenic Conference on Artificial Intelligence (SETN 2008), Springer, LNAI Vol.5138, pp. 401-406, Syros, Greece, 2008.
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| Abstract: |
Multilabel classification is a rapidly developing field of machine learning. Despite its short life, various methods for solving the task of multilabel classification have been proposed. In this paper we focus on a subset of these methods that adopt a lazy learning approach and are based on the traditional k-nearest neighbor (kNN) algorithm. Two are our main contributions. Firstly, we implement BRkNN, an adaptation of the kNN algorithm for multilabel classification that is conceptually equivalent to using the popular Binary Relevance problem transformation method in conjunction with the kNN algorithm, but much faster. We also identify two useful extensions of BRkNN that improve its overall predictive performance. Secondly, we compare this method against two other lazy multilabel classification methods, in order to determine the overall best performer. Experiments on different real-world multilabel datasets, using a variety of evaluation metrics, expose the advantages and limitations of each method with respect to specific dataset characteristics. |
| See also : |
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This paper has been cited by the following:
| 1 |
S. D. Villalba and P. Cunningham, “Using Unsupervised Classifiers for Multilabel Classification in Open-Class-Set Scenarios”, Proceedings of the ECML/PKDD 2009 Workshop on Learning from Multi-Label Data (MLD’09), pp. 146-160, Bled, Slovenia, September 2009. |
| 2 |
M. Atif Tahir, J. Kittler, F. Yan and K. Mikolajczyk, "Kernel Discriminant Analysis Using Triangular Kernel For Semantic Scene Classification", Proceedings of the 7th International Workshop on Content-Based Multimedia Indexing (CBMI 2009), 3-5 June 2009, Chania, Greece, IEEE, 2009. |
| 3 |
Zhang, X., Yuan, Q., Zhao, S., Fan, W. Zheng, W., Wang, Z. (2010) Multi-Label Classification Without Multi-Label Cost, Proc. 2010 SIAM International Conference on Data Mining (SDM 2010), pp. 778-789.
|
| 4 |
Heath, D., Zitzelberger, A., Giraud-Carrier, C.G. (2010) A Multiple Domain Comparison of Multi-label Classification Methods, Proc 2nd International Workshop on Learning from Multi-Label Data.
|
| 5 |
Read, J. (2010) Scalable Multi-Label Classification, PhD Thesis, University of Waikato.
|
| 6 |
Lin, X., Chen, X.-W. (2010) Mr.KNN: soft relevance for multi-label classification. In Proceedings of the 19th ACM international conference on Information and knowledge management (CIKM '10). ACM, New York, NY, USA, 349-358.
|
| 7 |
Sanden,C. (2010) An Empirical Evaluation of Computational and Perceptual Multi-Label Genre Classification on Music, Master Thesis, Department of Mathematics and Computer Science, University of Lethbridge, Canada. |
| 8 |
Sanden, C., Zhang, J. (2011) An Empirical Study of Multi-Label Classifiers for Music Tag Annotation, Proc. 12th International Society for Music Information Retrieval Conference, ISMIR'11, October 24-28, pp. 717-722
|
| 9 |
Ávila, J.L., Gibaja, E.L., Zafra, A., Ventura, S. (2011) A gene expression programming algorithm for multi-label classification, Journal of Multiple-Valued Logic and Soft Computing, 17 (2-3), pp. 183-206.
|
| 10 |
Wu, H., Qu, G., Zhang, H., Hartric, C. (2011) Constrained Multi-Label Classification: A Semidefinite Programming Approach, Proc. the 2011 International Conference on Data Mining, DMIN'11,
|
| 11 |
Xu, J. (2011) An Empirical Comparison of Weighting Functions for Multi-Label Distance Weighted K-Neared Neighbour Method, David Bracewell, et al. (Eds): AIAA 2011,CS and IT 03, pp. 13–20, 2011. |
| 12 |
Gjorgjevikj, D., Madjarov, G. (2011) Two stage classifier chain architecture for efficient pair-wise multi-label learning, IEEE International Workshop on Machine Learning for Signal Processing, art. no. 6064599, .
|
| 13 |
Read, J., Pfahringer, B., Holmes, G., Frank, E. (2011) Classifier chains for multi-label classification, Machine Learning, 85 (3), pp. 333-359.
|
| 14 |
Qu, G., Zhang, H., Hartrick, C.T. (2011) Multi-label classification with Bayes' theorem, Proceedings - 2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011, 4, art. no. 6098780, pp. 2281-2285.
|
| 15 |
Madjarov, G., Kocev, D., Gjorgjevikj, D., Džeroski, S. (2012) An extensive experimental comparison of methods for multi-label learning, Pattern Recognition, 45 (9), pp. 3084-3104.
|
| 16 |
Qu, G., Wu, H., Hartrick, C.T., Niu, J. (2012) Local analgesia adverse effects prediction using multi-label classification, Neurocomputing, 92, pp. 18-27.
|
| 17 |
De Ferrari,L.;Aitken,S.,van Hemert,J.;Goryanin,I. (2012) EnzML: multi-label prediction of enzyme classes using InterPro signatures, BMC Bioinformatics 2012 Apr 25;13:61.
|
| 18 |
Gjorgji Madjarov, Dejan Gjorgjevikj, Sašo Džeroski (2012) Two stage architecture for multi-label learning, Pattern Recognition, Volume 45, Issue 3, March 2012, Pages 1019-1034, ISSN 0031-3203
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