Improving multilabel classification performance by using ensemble of multi-label classifiers

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5997 LNCS, Issue , 2010, Pages 11-21

  Tahir, Muhammad Atif ^a   Kittler, Josef ^a   Mikolajczyk, Krystian ^a   Yan, Fei ^a  

^a UNIVERSITY OF SURREY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION);

ENSEMBLE APPROACHES; ENSEMBLE TECHNIQUES; EVALUATION CRITERIA; HETEROGENEOUS ENSEMBLES; MULTI-LABEL CLASSIFICATIONS; MULTI-LABEL LEARNING; RESEARCH EFFORTS; RESEARCH PROBLEMS;

LEARNING SYSTEMS;

EID: 77952042089     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-12127-2_2     Document Type: Conference Paper

References (18)

1. Li, T., Ogihara, M.: Toward intelligent music information retrieval. IEEE Trans. on Multimedia 8(3), 564-574 (2006) (Pubitemid 43751477)
2. Godbole, S., Sarawagi, S.: Discriminative methods for multi-labeled classification. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 22-30. Springer, Heidelberg (2004)
3. Zhang, M.L., Zhou, Z.H.: Multilabel neural networks with applications to functional genomics and text categorization. IEEE Trans. on Knowledge and Data Engineering 18(10), 1338-1351 (2006)
4. Tahir, M.A., Kittler, J., Yan, F., Mikolajczyk, K.: Kernel discriminant analysis using triangular kernel for semantic scene classification. In: Proc. of the 7th International Workshop on CBMI, Crete, Greece. IEEE, Los Alamitos (2009)
5. Dimou, A., Tsoumakas, G., Mezaris, V., Kompatsiaris, I., Vlahavas, I.: An empirical study of multi-label learning methods for video annotation. In: Proc. of the 7th International Workshop on CBMI, Chania, Greece (2009)
6. Chawla, N.V., Sylvester, J.C.: Exploiting diversity in ensembles: Improving the performance on unbalanced datasets. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 397-406. Springer, Heidelberg (2007)
7. Cheng, W., Hullermeier, E.: Combining instance-based learning and logistic regression for multilabel classification. Machine Learning 76(2-3), 211-225 (2009)
8. Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. In: Buntine,W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009, Part II. LNCS (LNAI), vol. 5782, pp. 254-269. Springer, Heidelberg (2009)
9. Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining Multi-label Data. In: Data Mining and Knowledge Discovery Handbook, 2nd edn. Springer, Heidelberg (2009)
10. Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recognition 37(9), 1757-1771 (2004)
11. Tsoumakas, G., Vlahavas, I.: Random k-labelsets: An ensemble method for multi-label classification. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 406-417. Springer, Heidelberg (2007)
12. Furnkranz, J., Hullermeier, E., Menca, E.L., Brinker, K.: Multilabel classification via calibrated label ranking. Machine Learning 23(2), 133-153 (2008)
13. Zhang, M.L., Zhou, Z.H.: ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognition 40(7), 2038-2048 (2007)
14. Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. In: Advances in NIPS, vol. 14 (2002)
15. Read, J., Pfahringer, B., Holmes, G.: Multi-label classification using ensembles of pruned sets. In: Perner, P. (ed.) ICDM 2008. LNCS (LNAI), vol. 5077. Springer, Heidelberg (2008)
16. Kuncheva, L.I.: Combining Pattern Classifiers. Wiley, Chichester (2004)
17. Fan, R.E., Lin, C.J.: A study on threshold selection for multi-label classification. Technical report, National Taiwan University (2007)
18. Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco (2005)