Obtaining bipartitions from score vectors for multi-label classification

Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

Volumn 1, Issue , 2010, Pages 409-416

  Ioannou, Marios ^a   Sakkas, George ^a   Tsoumakas, Grigorios ^a   Vlahavas, Ioannis ^a  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

BIPARTITION; COMPARATIVE STUDIES; HIGH QUALITY; LEARNING TASKS; MULTI-LABEL; MULTI-LABEL CLASSIFICATIONS; RECENT STATE; THRESHOLDING; THRESHOLDING METHODS;

ARTIFICIAL INTELLIGENCE;

ALGORITHMS;

EID: 78751507384     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICTAI.2010.65     Document Type: Conference Paper

References (35)

1. A. McCallum, "Multi-label text classification with a mixture model trained by em," in Proceedings of the AAAI' 99 Workshop on Text Learning, 1999.
2. Y. Schapire, R.E. Singer, "Boostexter: a boosting-based system for text categorization," Machine Learning, vol. 39, no. 2/3, pp. 135-168, 2000.
3. K. Crammer and Y. Singer, "A family of additive online algorithms for category ranking," Journal of Machine Learning Research, vol. 3, pp. 1025-1058, 2003.
4. J. Rousu, C. Saunders, S. Szedmak, and J. Shawe-Taylor, "Kernel-based learning of hierarchical multilabel classification methods," Journal of Machine Learning Research, vol. 7, pp. 1601-1626, 2006.
5. N. Cesa-Bianchi, C. Gentile, and L. Zaniboni, "Hierarchical classification: combining bayes with svm," in ICML '06: Proceedings of the 23rd international conference on Machine learning, 2006, pp. 177-184.
6. M. Boutell, J. Luo, X. Shen, and C. Brown, "Learning multi-label scene classification," Pattern Recognition, vol. 37, no. 9, pp. 1757-1771, 2004.
7. T. Li and M. Ogihara, "Toward intelligent music information retrieval," IEEE Transactions on Multimedia, vol. 8, no. 3, pp. 564-574, 2006.
8. K. Trohidis, G. Tsoumakas, G. Kalliris, and I. Vlahavas, "Multilabel classification of music into emotions," in Proc. 9th International Conference on Music Information Retrieval (ISMIR 2008), Philadelphia, PA, USA, 2008, 2008.
9. A. Dimou, G. Tsoumakas, V. Mezaris, I. Kompatsiaris, and I. Vlahavas, "An empirical study of multi-label learning methods for video annotation," in Proc. 7th International Workshop on Content-Based Multimedia Indexing, CBMI '09, Chania, Greece, 2009.
10. G.-J. Qi, X.-S. Hua, Y. Rui, J. Tang, T. Mei, and H.-J. Zhang, "Correlative multi-label video annotation," in MULTIMEDIA '07: Proceedings of the 15th international conference on Multimedia. NewYork,NY, USA: ACM, 2007, pp. 17-26.
11. A. Clare and R. King, "Knowledge discovery in multi-label phenotype data," in Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD 2001), Freiburg, Germany, 2001, pp. 42-53.
12. A. Elisseeff and J. Weston, "A kernel method for multi-labelled classification," in Advances in Neural Information Processing Systems 14, 2002.
13. H. Blockeel, L. Schietgat, J. Struyf, S. Dzeroski, and A. Clare, "Decision trees for hierarchical multilabel classification: A case study in functional genomics," in Proc. 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2006, 2006, pp. 18-29.
14. C. Vens, J. Struyf, L. Schietgat, S. Džeroski, and H. Blockeel, "Decision trees for hierarchical multi-label classification," Machine Learning, vol. 73, no. 2, pp. 185-214, 2008.
15. G. Pandey, C. L. Myers, and V. Kumar, "Incorporating functional inter-relationships into protein function prediction algorithms," BMC Bioinformatics, vol. 10, no. 1, p. 142, 2009.
16. Y. Zhang, S. Burer, and W. N. Street, "Ensemble pruning via semi-definite programming," Journal of Machine Learning Research, vol. 7, pp. 1315-1338, 2006.
17. Y. Yang, "A study of thresholding strategies for text categorization," in SIGIR '01: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval. New York, NY, USA: ACM, 2001, pp. 137-145.
18. R.-E. Fan and C. J. Lin, "A study on threshold selection for multi-label classification," National Taiwan University, Tech. Rep., 2007.
19. E. Hüllermeier, J. Fürnkranz, W. Cheng, and K. Bringer, "Label ranking by learning pairwise preferences," Artificial Intelligence, 2008.
20. M.-L. Zhang and Z.-H. Zhou, "Multi-label neural networks with applications to functional genomics and text categorization," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 10, pp. 1338-1351, 2006.
21. G. Tsoumakas, I. Katakis, and I. Vlahavas, "Random k-labelsets for multi-label classification," IEEE Transactions on Knowledge and Data Engineering, 2010, (accepted).
22. J. Read, B. Pfahringer, and G. Holmes, "Multi-label classification using ensembles of pruned sets," in Proc. 8th IEEE International Conference on Data Mining (ICDM'08), 2008, pp. 995-1000.
23. J. Read, B. Pfahringer, G. Holmes, and E. Frank, "Classifier chains for multi-label classification," in Proc. 20th European Conference on Machine Learning (ECML 2009), 2009, pp. 254-269.
24. M.-L. Zhang and Z.-H. Zhou, "Ml-knn: A lazy learning approach to multi-label learning," Pattern Recognition, vol. 40, no. 7, pp. 2038-2048, 2007.
25. J. Fürnkranz, E. Hüllermeier, E. L. Mencia, and K. Brinker, "Multilabel classification via calibrated label ranking," Machine Learning, 2008.
26. L. Tang, S. Rajan, and V. K. Narayanan, "Large scale multi-label classification via metalabeler," in WWW '09: Proceedings of the 18th international conference on World wide web. New York, NY, USA: ACM, 2009, pp. 211-220.
27. G. Tsoumakas, I. Katakis, and I. Vlahavas, "Mining multi-label data (accepted)," in Data Mining and Knowledge Discovery Handbook, 2nd ed., O. Maimon and L. Rokach, Eds. Springer, 2009.
28. G. Tsoumakas and I. Vlahavas, "Random k-labelsets: An ensemble method for multilabel classification," in Proceedings of the 18th European Conference on Machine Learning (ECML 2007), Warsaw, Poland, September 17-21 2007, pp. 406-417.
29. I. Katakis, G. Tsoumakas, and I. Vlahavas, "Multilabel text classification for automated tag suggestion," in Proceedings of the ECML/PKDD 2008 Discovery Challenge, Antwerp, Belgium, 2008.
30. Y. Song, L. Zhang, and L. C. Giles, "A sparse gaussian processes classification framework for fast tag suggestions," in CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge management. ACM, 2008, pp. 93-102.
31. J. Read, "A pruned problem transformation method for multi-label classification," in Proc. 2008 New Zealand Computer Science Research Student Conference (NZCSRS 2008), 2008, pp. 143-150.
32. L. Breiman, "Bagging predictors," Machine Learning, vol. 24, no. 2, pp. 123-140, 1996.
33. R. Quinlan, C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann Publishers, 1993.
34. Y. Wang and I. H. Witten, "Induction of model trees for predicting continuous classes," in Poster papers of the 9th European Conference on Machine Learning. Springer, 1997.
35. I. H. Witten and E. Frank, Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2005.