A tutorial on multi-label classification techniques

Studies in Computational Intelligence

Volumn 205, Issue , 2009, Pages 177-195

  de Carvalho, André C P L F ^a   Freitas, Alex A ^b  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

^b UNIVERSITY OF KENT   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 67949108237     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-01536-6_8     Document Type: Article

References (54)

1. Aiolli, F., Sperduti, A.: Multiclass Classification with Multi-Prototype Support Vector Machines. Journal of Machine Learning Research 6, 817-850 (2005)
2. Barlett, P., Peter, B., Bartlett, J., Schölkopf, B., Schuurmans, D., Smola, A.J.: Advances in Large-Margin Classifiers. The MIT Press, Cambridge (2000)
3. Barutcuoglu, Z., Schapire, R.E., Troyanskaya, O.G.: Hierarchical multi-label prediction of gene function. Bioinformatics 22, 830-836 (2006)
4. Boutell, M., Shen, X., Luo, J., Brown, C.: Multi-label semantic scene classification. Technical Report, Department of Computer Science University of Rochester, USA (2003)
5. Blockeel, H., Bruynooghe, M., Dzeroski, S., Ramon, J., Struyf, J.: Hierarchical multiclassication. In: Proceedings of the ACM SIGKDD 2002 Workshop on Multi-Relational Data Mining (MRDM 2002), Edmonton, Canada, pp. 21-35.
6. Blockeel, H., Schietgat, L., Struyf, J., Dzeroski, S., Clare, A.: Decision Trees for Hierarchical Multilabel Classification: A Case Study in Functional Genomics. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS, vol. 4213, pp. 18-29. Springer, Heidelberg (2006)
7. Brinker, K., Fürnkranz, J., Hüllermeier, E.: A Unified Model for Multila-bel Classification and Ranking. In: ECAI 2006, pp. 489-493 (2006)
8. Brinker, K., Hüllermeier, E.: Case-Based Multilabel Ranking. In: IJCAI, pp. 702-707 (2007)
9. Brinker, K., Hüllermeier, E.: Label Ranking in Case-Based Reasoning. In: Weber, R.O., Richter, M.M. (eds.) ICCBR 2007. LNCS, vol. 4626, pp. 77-91. Springer, Heidelberg (2007)
10. Chan, A., Freitas, A.A.: A new ant colony algorithm for multi-label classi-fication with applications in bioinfomatics. In: Genetic and Evolutionary Computation 2006 Conference (GECCO 2006), Seattle, USA, pp. 27-34 (2006)
11. Clare, A.J., King, R.D.: Knowledge discovery in multi-label phenotype data. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, p. 42. Springer, Heidelberg (2001)
12. de Comite, F., Gilleron, R., Tommasi, M.: Learning Multi-label Alter-nating Decision Trees from Texts and Data. In: Perner, P., Rosenfeld, A. (eds.) MLDM 2003. LNCS, vol. 2734, pp. 251-274. Springer, Heidelberg (2003)
13. Elisseeff, A., Weston, J.: Kernel methods for multi-labelled classifica-tion and categorical regression problems. Technical Report. BIOwulf Technologies (2001)
14. Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. In: Neural Information processing Systems. NIPS, vol. 14 (2001)
15. Freitas, A.A., de Carvalho, A.C.P.L.F.: A Tutorial on Hierarchical Clas-sification with Applications in Bioinformatics. In: Taniar, D. (ed.) Research and Trends in Data Mining Technologies and Applications, pp. 175-208. Idea Group (2007)
16. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P.M.B. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23-37. Springer, Heidelberg (1995)
17. Freund, Y., Mason, L.: The alternating decision tree learning algorithm. In: Proceedings of the Sixteenth International Conference on Machine Learning, ICML, pp. 124-133 (1999)
18. Gao, S., Wu, W., Lee, C.-H., Chua, T.-S.: An MFoM Learning Approach to Robust Multiclass Multi-Label Text Categorization. In: Proceedings of the International Conference on Machine Learning (ICML 2004), Banff, Canada, pp. 329-336 (2004)
19. Ghamrawi, N., McCallum, A.: Collective Multi-Label Classification. In: Proceedings of the Fourteenth Conference on Information and Knowledge Management (CIKM), pp. 195-200 (2005)
20. Godbole, S., Sarawagi, S.: Discriminative Methods for Multi-labeled Clas-sification. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS, vol. 3056, pp. 22-30. Springer, Heidelberg (2004)
21. Gonçalves, T., Quaresma, P.: A preliminary approach to the multi-label classification problem of Portuguese juridical documents. In: Pires, F.M., Abreu, S.P. (eds.) EPIA 2003. LNCS (LNAI), vol. 2902, pp. 435-444. Springer, Heidelberg (2003)
22. Hua, X., Qi, G.: Online multi-laTel active annotation: towards large-scale contentbased video search. In: Proceeding of the 16th ACM international Conference on Multimedia. MM 2008, Vancouver, British Columbia, Canada, October 26 - 31, pp. 141-150. ACM, New York (2008)
23. Hsu, C.-W., Lin, C.-J.: A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks 13(2), 415-425 (2002)
24. Joachims, T.: Text categorization with suppoLt vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137-142. Springer, Heidelberg (1998)
25. Karaliç, A., Pirnat, V.: Significance level based multiple tree classification. Informatica 15(5), 12 Pages (1991)
26. Lauser, B., Hotho, A.: Automatic multi-label subject indexing in a multi-lingual environment. In: Koch, T., Solvberg, I.T. (eds.) ECDL 2003. LNCS, vol. 2769, pp. 140-151. Springer, Heidelberg (2003)
27. Li, T., Zhang, C., Zhu, S.: Empirical Studies on Multi-label Classification. In: Proceedings of the 18th IEEE international Conference on Tools with Artificial intelligence. ICTAI, November 13 - 15, pp. 86-92. IEEE Computer Society, Washington (2006)
28. Luo, X., Zincir-Heywood, A.N.: Evaluation of Two Systems on Multi-class Multi-label Document Classification. In: Hacid, M.-S., Murray, N.V., Ras, Z.W., Tsumoto, S. (eds.) ISMIS 2005. LNCS (LNAI), vol. 3488, pp. 161-169. Springer, Heidelberg (2005)
29. McDonald, R., Crammer, K., Pereira, F.: Flexible Text Segmentation with Structured Multilabel Classification. In: Proceedings of the Human Language Technology Conference on Empirical Methods in Natural Language Processing (HLT-EMNLP, 2005), Vancouver, Canada (2005)
30. McCallum, A.: Multi-label text classification with a mixture model trained by EM. In: AAAI 1999 Workshop on Text Learning (1999)
31. Micchelli, C.A., Pontil, M.: Kernels for Multi-task Learning. In: Saul, L.K., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems. NIPS 2004, vol. 17, pp. 921-928. MIT Press, Cambridge (2005)
32. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
33. Pavlidis, P., Weston, J., Cai, J., Grundy, W.: Combining microarray expression data and phylogenetic profiles to learn functional categories using support vector machines. In: RECOMB, pp. 242-248 (2001)
34. Rousu, J., Saunders, C., Szedmak, S., Shawe-Taylor, J.: Learning Hierarchical Multi- Category Text Classification Models. In: 22nd International Conference on Machine Learning (ICML 2005), Bonn, Germany, pp. 745-752 (2005)
35. Rousu, J., Saunders, C., Szedmak, S., Shawe-Taylor, J.: Kernel-based Learning of Hierarchical Multilabel Classification Models. Journal of Machine Learning Research 7, 1601-1626 (2006)
36. Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1-47 (2002)
37. Shen, X., Boutell, M., Luo, J., Brown, C.: Multi-label machine learning and its application to semantic scene classification. Storage and Retrieval Methods and Applications for Multimedia. In: Yeung, M.M., Lienhart, R.W., Li, C.-S. (eds.) Proceedings of the SPIE, vol. 5307, pp. 188-199 (2003)
38. Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Machine Learning 37(3), 297-336 (1999)
39. Schapire, R.E., Singer, Y.: BoosTexter: A boosting-based system for text categorization. Machine Learning 39(2/3), 135-168 (2000)
40. Su, C.-Y., Lo, A., Lin, C.-C., Chang, F., Hsu, W.-L.: A Novel Approach for Prediction of Multi-Labeled Protein Subcellular Localization for Prokaryotic Bacteria. In: Computational Systems Bioinformatics Conference, CSB Workshops, Palo Alto, USA, pp. 79-82 (2005)
41. Thabtah, F.A., Cowling, P., Peng, Y.: MMAC: A New Multi-Class, Multi-Label Associative Classification Approach. In: Perner, P. (ed.) ICDM 2004. LNCS, vol. 3275, pp. 217-224. Springer, Heidelberg (2004)
42. Tikk, D., Biró, G.: Experiments with multi-label text classifier on the Reuters collection. In: Proc. of the International Conference on Computational Cybernetics (ICCC 2003), Siófok, Hungary, pp. 33-38 (2003)
43. Tsoumakas, G., Katakis, I.: Multi-Label Classification: An Overview. International Journal of Data Warehousing and Mining 3(3), 1-13 (2007)
44. Ueda, N., Saito, K.: Parametric mixture models for multi-topic text. In: Neural Information Processing Systems 15 (NIPS 15), pp. 737-744. MIT Press, Cambridge (2002)
45. Ueda, N., Saito, K.: Single-shot detection of multi-category text using pa-rametric mixture models. In: ACM SIG Knowledge Discovery and Data Mining (SIGKDD 2002), pp. 626-631 (2002)
46. Vallim, R.M.M., Goldberg, D.E., Llorà, X., Duque, T.S.P.C.: A New Approach for Multi-label Classification Based on Default Hierarchies and Organizational Learning, IWLCS. In: The 11th International Workshop on Learning Classifier Systems, part of the Genetic and Evolutionary Computation 2008 Conference (GECCO 2008), Atlanta, Georgia, USA (accepted) (2008)
47. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)
48. Xu, Y.-Y., Zhou, X.-Z., Guo, Z.-W.: Weak learning algorithm for multi-label multiclass text categorization. In: International Conference on Machine Learning and Cybernetics, 2002. Proceedings, vol. 2, pp. 890-894 (2002)
49. Yan, R., Tesic, J., Smith, J.R.: Model-shared subspace boosting for multi-label classification. In: Proceedings of the 13th ACM SIGKDD international Con-ference on Knowledge Discovery and Data Mining. KDD 2007, San Jose, California, USA, August 12-15, pp. 834-843. ACM, New York (2007)
50. Yu, K., Yu, S., Tresp, V.: Multi-label informed latent semantic indexing. In: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 258-265 (2005)
51. Zhang, M.-L., Zhou, Z.-H.: A k-nearest neighbor based algorithm for multi-label classification. In: Proceedings of the 1st IEEE International Conference on Granular Computing (GrC 2005), Beijing, China, pp. 718-721 (2005)
52. Zhou, Z.: Mining Ambiguous Data with Multi-instance Multi-label Representation. In: Alhajj, R., Gao, H., Li, X., Li, J., Zaiane, O.R. (eds.) ADMA 2007. LNCS, vol. 4632, p. 1. Springer, Heidelberg (2007)
53. Zhu, B., Poon, C.K.: Efficient Approximation Algorithms for Multi-label Map Labeling. In: Aggarwal, A.K., Pandu Rangan, C. (eds.) ISAAC 1999. LNCS, vol. 1741, pp. 143-152. Springer, Heidelberg (1999)
54. Zhu, S., Ji, X., Xu, W., Gong, Y.: Multi-labeled Classification Using Maximum Entropy Method. In: Proceedings of Annual ACM Conference on Research and Development in Information Retrieval (SIGIR 2005), pp. 274-281, Salvador, Brazil (2005)