Multi-label classification by exploiting label correlations

Expert Systems with Applications

Volumn 41, Issue 6, 2014, Pages 2989-3004

  Yu, Ying ^a,b,c,e   Pedrycz, Witold ^b,d   Miao, Duoqian ^a,c  

^a TONGJI UNIVERSITY   (China)

^b UNIVERSITY OF ALBERTA   (Canada)

^c Department of Computer Science and Technology   (China)

^d SYSTEMS RESEARCH INSTITUTE   (Poland)

^e JIANGXI AGRICULTURAL UNIVERSITY   (China)

Author keywords

Correlation; Multi label classification; Rough sets; Uncertainty

Indexed keywords

CLASSIFICATION METHODS; LOCAL CORRELATIONS; MULTI-LABEL CLASSIFICATIONS; MULTI-LABEL LEARNING; NEIGHBORHOOD ROUGH SETS; TEXT CATEGORIZATION; UNCERTAINTY; VARIABLE PRECISION;

CORRELATION METHODS; LEARNING ALGORITHMS; TEXT PROCESSING;

ROUGH SET THEORY;

EID: 84890571220     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2013.10.030     Document Type: Article

References (31)

1. M.R. Boutell, J. Luo, and X. Shen Learning multi-label scene classification Pattern Recognition 37 9 2004 1757 1771
2. F. De Comité, R. Gilleron, and M. Tommasi Learning multi-label alternating decision trees from texts and data Machine learning and data mining in pattern recognition 2003 Springer Berlin Heidelberg 35 49
3. T. Denœux, Z. Younes, and F. Abdallah Representing uncertainty on set-valued variables using belief functions Artificial Intelligence 174 7 2010 479 499
4. P. Duygulu, K. Barnard, and J.F.G. de Freitas Object recognition as chine translation: Learning a lexicon for a fixed image vocabulary Computer vision-ECCV 2002 2006 Springer Berlin Heidelberg 97 112
5. Elisseeff, A., & Weston, J. (2001). A kernel method for multi-labelled classification. In Advances in neural information processing systems (pp. 681-687).
6. Fan, R. E., Lin, C. J. (2007). A study on threshold selection for multi-label classification. Department of Computer Science, National Taiwan University.
7. S. Godbole, and S. Sarawagi Discriminative methods for multi-labeled classification Advances in knowledge discovery and data mining 2004 Springer Berlin Heidelberg 22 30
8. Q. Hu, J. Liu, and D. Yu Mixed feature selection based on granulation and approximation Knowledge-Based Systems 21 4 2008 294 304
9. Q. Hu, D. Yu, and Z. Xie Neighborhood classifiers Expert Systems with Applications 34 2 2008 866 876
10. E. Hüllermeier, J. Fürnkranz, and W. Cheng Label ranking by learning pairwise preferences Artificial Intelligence 172 16 2008 1897 1916
11. J.Y. Jiang, S.C. Tsai, and S.J. Lee FSKNN: Multi-label text categorization based on fuzzy similarity and k nearest neighbors Expert Systems with Applications 39 3 2012 2813 2821
12. Z. Pawlak, J. Grzymala-Busse, and R. Slowinski Rough sets Communications of the ACM 38 11 1995 88 95
13. Petrovskiy, M. (2006). Paired comparisons method for solving multi-label learning problem. In Hybrid intelligent systems, 2006. HIS'06. sixth international conference on. IEEE (pp. 42-42).
14. J. Read, B. Pfahringer, and G. Holmes Classifier chains for multi-label classification Machine learning and knowledge discovery in databases 2009 Springer Berlin Heidelberg 254 269
15. J. Read, B. Pfahringer, and G. Holmes Classifier chains for multi-label classification Machine Learning 85 3 2011 333 359
16. R.E. Schapire, and Y. Singer BoosTexter: A boosting-based system for text categorization Machine learning 39 2-3 2000 135 168
17. C.G.M. Snoek, M. Worring, and J.C. Van Gemert he challenge problem for automated detection of 101 semantic concepts in multimedia Proceedings of the 14th annual ACM international conference on multimedia 2006 ACM 421 430
18. E. Spyromitros, G. Tsoumakas, and I. Vlahavas An empirical study of lazy multilabel classification algorithms Artificial Intelligence. Theories, Models and Applications 2008 Springer Berlin Heidelberg 401 406
19. A.P. Streich, and J.M. Buhmann Classification of multi-labeled data: A generative approach Machine learning and knowledge discovery in databases 2008 Springer Berlin Heidelberg 390 405
20. K.T.G. Tsoumakas, G. Kalliris, and I. Vlahavas Multi-label classification of music into emotions ISMIR 2008: Proceedings of the 9th international conference of music information retrieval 2008 Lulu. com 325
21. G. Tsoumakas, I. Katakis, and I. Vlahavas Mining multi-label data Data mining and knowledge discovery handbook 2010 Springer US 667 685
22. G. Tsoumakas, I. Katakis, and I. Vlahavas Random k-labelsets for multilabel classification Knowledge and Data Engineering, IEEE Transactions on 23 7 2011 1079 1089
23. G. Tsoumakas, and I. Vlahavas Random k-labelsets: An ensemble method for multilabel classification Machine learning: ECML 2007 2007 Springer Berlin Heidelberg 406 417
24. G. Tsoumakas, E.S. Xioufis, and J. Vilcek MULAN: A java library for multi-label learning Journal of Machine Learning Research 12 7 2011 2411 2414
25. D. Turnbull, L. Barrington, and D. Torres Semantic annotation and retrieval of music and sound effects Audio, Speech, and Language Processing, IEEE Transactions on 16 2 2008 467 476
26. C. Vens, J. Struyf, and L. Schietgat Decision trees for hierarchical multi-label classification Machine Learning 73 2 2008 185 214
27. I.H. Witten, E. Frank, and M.A. Hall Data mining: Practical machine learning tools and techniques 2011 Elsevier
28. Y.Y. Yao Relational interpretations of neighborhood operators and rough set approximation operators Information Sciences 111 1 1998 239 259
29. Y. Yu, W. Pedrycz, and D. Miao Neighborhood rough sets based multi-label classification for automatic image annotation International Journal of Approximate Reasoning 2013
30. M.L. Zhang, and Z.H. Zhou Multilabel neural networks with applications to functional genomics and text categorization Knowledge and Data Engineering, IEEE Transactions on 18 10 2006 1338 1351
31. M.L. Zhang, and Z.H. Zhou ML-KNN: A lazy learning approach to multi-label learning Pattern Recognition 40 7 2007 2038 2048