1. Alcala-Fdez, J., Fernández, A., Luengo, J., Derrac, J., García, S., Sánchez, L., Herrera, F.: KEEL multi-label dataset repository.http://sci2s.ugr.es/keel/multilabel.php

2. Alcala-Fdez, J., Fernández, A., Luengo, J., Derrac, J., García, S., Sánchez, L., Herrera, F.: KEEL data-mining software tool: data set repository and integration of algorithms and experimental analysis framework. J. Mult-valued Log. Soft Comput.17(2–3), 255–287 (2011)

   Google Scholar 

3. Alvares-Cherman, E., Metz, J., Monard, M.C.: Incorporating label dependency into the binary relevance framework for multi-label classification. Expert Syst. Appl.39(2), 1647–1655 (2012)

   Article  Google Scholar 

4. Aly, M.: Survey on multiclass classification methods. In: Technical Report, pp. 1–9. California Institute of Technology (2005)

   Google Scholar 

5. Boutell, M., Luo, J., Shen, X., Brown, C.: Learning multi-label scene classification. Pattern Recogn.37(9), 1757–1771 (2004)

   Article  Google Scholar 

6. Breiman, L.: Bagging predictors. Mach. Learn.24(2), 123–140 (1996)

   MathSciNet MATH  Google Scholar 

7. Briggs, F., Lakshminarayanan, B., Neal, L., Fern, X.Z., Raich, R., Hadley, S.J.K., Hadley, A.S., Betts, M.G.: Acoustic classification of multiple simultaneous bird species: a multi-instance multi-label approach. J. Acoust. Soc. Am.131(6), 4640–4650 (2012)

   Article  Google Scholar 

8. Brinker, K., Hüllermeier, E.: Case-based multilabel ranking. In: Proceedings of 20th International Joint Conference on Artificial Intelligence, IJCAI’07, pp. 702–707. Morgan Kaufmann (2007)

   Google Scholar 

9. Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc.2(2), 121–167 (1998)

   Article  Google Scholar 

10. Charte, F., Charte, D.: Working with multilabel datasets in R: the mldr package. R J.7(2), 149–162 (2015)

    Google Scholar 

11. Charte, F., Charte, D., Rivera, A.J., del Jesus, M.J., Herrera, F.: R Ultimate multilabel dataset repository. In: Proceedings of 11th International Conference on Hybrid Artificial Intelligent Systems, HAIS’16, vol. 9648, pp. 487–499. Springer (2016)

    Google Scholar 

12. Charte, F., Rivera, A.J., del Jesus, M.J., Herrera, F.: Multilabel classification. In: Problem Analysis, Metrics and Techniques Book Repository.https://github.com/fcharte/SM-MLC

13. Charte, F., Rivera, A.J., del Jesus, M.J., Herrera, F.: Addressing imbalance in multilabel classification: measures and random resampling algorithms. Neurocomputing163, 3–16 (2015)

    Article  Google Scholar 

14. Charte, F., Rivera, A.J., del Jesus, M.J., Herrera, F.: MLSMOTE: approaching imbalanced multilabel learning through synthetic instance generation. Knowl.-Based Syst.89, 385–397 (2015)

    Article  Google Scholar 

15. Charte, F., Rivera, A.J., del Jesus, M.J., Herrera, F.: QUINTA: a question tagging assistant to improve the answering ratio in electronic forums. In: Proceedings of IEEE International Conference on Computer as a Tool, EUROCON’15, pp. 1–6. IEEE (2015)

    Google Scholar 

16. Chen, K., Lu, B., Kwok, J.: Efficient classification of multi-label and imbalanced data using min-max modular classifiers. In: Proceedings of IEEE International Joint Conference on Neural Networks, IJCNN’06, pp. 1770–1775 (2006)

    Google Scholar 

17. Chen, X., Zhan, Y., Ke, J., Chen, X.: Complex video event detection via pairwise fusion of trajectory and multi-label hypergraphs. Multimedia Tools Appl. 1–22 (2015)

    Google Scholar 

18. Cherkassky, V., Mulier, F.: Learning from Data: Concepts. Theory and Methods. Wiley-IEEE Press (2007)

    Google Scholar 

19. Cong, H., Tong, L.H.: Grouping of triz inventive principles to facilitate automatic patent classification. Expert Syst. Appl.34(1), 788–795 (2008)

    Article  Google Scholar 

20. Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory13(1), 21–27 (1967)

    Article MATH  Google Scholar 

21. Crammer, K., Dredze, M., Ganchev, K., Talukdar, P.P., Carroll, S.: Automatic code assignment to medical text. In: Proceedings of Workshop on Biological, Translational, and Clinical Language Processing, BioNLP’07, pp. 129–136. Association for Computational Linguistics (2007)

    Google Scholar 

22. Dembszynski, K., Waegeman, W., Cheng, W., Hüllermeier, E.: On label dependence in multilabel classification. In: ICML Workshop on Learning from Multi-label data, pp. 5–12 (2010)

    Google Scholar 

23. Dendamrongvit, S., Kubat, M.: Undersampling approach for imbalanced training sets and induction from multi-label text-categorization domains. In: New Frontiers in Applied Data Mining,LNCS, vol. 5669, pp. 40–52. Springer (2010)

    Google Scholar 

24. Dietterich, T.: Ensemble methods in machine learning. In: Multiple Classifier Systems.LNCS, vol. 1857, pp. 1–15. Springer (2000)

    Google Scholar 

25. Diplaris, S., Tsoumakas, G., Mitkas, P., Vlahavas, I.: Protein classification with multiple algorithms. In: Proc. 10th Panhellenic Conference on Informatics, PCI’05, vol. 3746, pp. 448–456. Springer (2005)

    Google Scholar 

26. Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. John Wiley (2000)

    Google Scholar 

27. Dumais, S., Furnas, G., Landauer, T., Deerwester, S., Deerwester, S., et al.: Latent semantic indexing. In: Proceedings of 4th Text Retrieval Conference, TREC-4, pp. 105–115. NIST (1995)

    Google Scholar 

28. Duygulu, P., Barnard, K., de Freitas, J., Forsyth, D.: Object recognition as machine translation: learning a lexicon for a fixed image vocabulary. In: Proceedings of 7th European Conference on Computer Vision, ECCV’02, vol. 2353, pp. 97–112. Springer (2002)

    Google Scholar 

29. Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. In: Advances in Neural Information Processing Systems, vol. 14, pp. 681–687. MIT Press (2001)

    Google Scholar 

30. Fürnkranz, J., Hüllermeier, E., Loza Mencía, E., Brinker, K.: Multilabel classification via calibrated label ranking. Mach. Learn.73, 133–153 (2008)

    Google Scholar 

31. Galar, M., Fernández, A., Barrenechea, E., Bustince, H., Herrera, F.: An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes. Pattern Recogn.44(8), 1761–1776 (2011)

    Article  Google Scholar 

32. Galar, M., Fernández, A., Barrenechea, E., Bustince, H., Herrera, F.: A review on ensembles for the class imbalance problem: bagging, boosting, and hybrid-based approaches. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev.42(4), 463–484 (2012)

    Article  Google Scholar 

33. Ghamrawi, N., McCallum, A.: Collective multi-label classification. In: Proceedings of 14th ACM International Conference on Information and Knowledge Management, CIKM’05, pp. 195–200. ACM (2005)

    Google Scholar 

34. Gibaja, E., Ventura, S.: A tutorial on multi-label learning. ACM Comput. Surv.47(3) (2015)

    Google Scholar 

35. Gonçalves, T., Quaresma, P.: A preliminary approach to the multilabel classification problem of Portuguese juridical documents. In: Proceedings of 11th Portuguese Conference on Artificial Intelligence, EPIA’03, pp. 435–444. Springer (2003)

    Google Scholar 

36. Guo, Y., Gu, S.: Multi-label classification using conditional dependency networks. In: Proceedings of 22th International Joint Conference on Artificial Intelligence,IJCAI’11, vol. 2, pp. 1300–1305 (2011)

    Google Scholar 

37. Holmes, G., Donkin, A., Witten, I.H.: WEKA: a machine learning workbench. In: Proceedings of 2nd Australian and New Zealand Conference on Intelligent Information Systems, ANZIIS’02, pp. 357–361 (2002)

    Google Scholar 

38. Hüllermeier, E., Fürnkranz, J., Cheng, W., Brinker, K.: Label ranking by learning pairwise preferences. Artif. Intell.172(16), 1897–1916 (2008)

    Article MathSciNet MATH  Google Scholar 

39. Jolliffe, I.: Introduction. In: Principal Component Analysis, pp. 1–7. Springer (1986)

    Google Scholar 

40. Katakis, I., Tsoumakas, G., Vlahavas, I.: Multilabel text classification for automated tag suggestion. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD’08, pp. 75–83 (2008)

    Google Scholar 

41. Klimt, B., Yang, Y.: The enron corpus: a new dataset for email classification research. In: Proc eedings of 15th European Conference on Machine Learning, ECML’04, pp. 217–226. Springer (2004)

    Google Scholar 

42. Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: RCV1: a new benchmark collection for text categorization research. J. Mach. Learn. Res.5, 361–397 (2004)

    Google Scholar 

43. Read, J., Pfahringer, B., Holmes, G.: Multi-label classification using ensembles of pruned sets. In: Proc eedings of 8th IEEE International Conference on Data Mining, ICDM’08, pp. 995–1000. IEEE (2008)

    Google Scholar 

44. Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn.85, 333–359 (2011)

    Article MathSciNet  Google Scholar 

45. Read, J., Reutemann, P.: MEKA multi-label dataset repository.http://sourceforge.net/projects/meka/files/Datasets/

46. Rokach, L.: Pattern classification using ensemble methods. World Scientific (2009)

    Google Scholar 

47. Salton, G., Fox, E.A., Wu, H.: Extended Boolean information retrieval. Commun. ACM26(11), 1022–1036 (1983)

    Article MathSciNet MATH  Google Scholar 

48. Snoek, C.G.M., Worring, M., van Gemert, J.C., Geusebroek, J.M., Smeulders, A.W.M.: The challenge problem for automated detection of 101 semantic concepts in multimedia. In: Proceedings of 14th ACM International Conference on Multimedia, MULTIMEDIA’06, pp. 421–430 (2006)

    Google Scholar 

49. Sobol-Shikler, T., Robinson, P.: Classification of complex information: Inference of co-occurring affective states from their expressions in speech. IEEE Trans. Pattern Anal. Mach. Intell.32(7), 1284–1297 (2010)

    Article  Google Scholar 

50. Sriram, B., Fuhry, D., Demir, E., Ferhatosmanoglu, H., Demirbas, M.: Short text classification in twitter to improve information filtering. In: Proceedings of 33rd international ACM SIGIR conference on Research and development in information retrieval, pp. 841–842. ACM (2010)

    Google Scholar 

51. Sun, L., Ji, S., Ye, J.: Multi-label dimensionality reduction. CRC Press (2013)

    Google Scholar 

52. Tenenboim-Chekina, L., Rokach, L., Shapira, B.: Identification of label dependences for multi-label classification. In: Proceedings of 2nd International Workshop on Learning from Multi-Label Data, MLD’10, pp. 53–60 (2010)

    Google Scholar 

53. Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Data Mining and Knowledge Discovery Handbook, pp. 667–685. Springer (2010)

    Google Scholar 

54. Tsoumakas, G., Xioufis, E.S., Vilcek, J., Vlahavas, I.: MULAN multi-label dataset repository.http://mulan.sourceforge.net/datasets.html

55. Tsoumakas, G., Xioufis, E.S., Vilcek, J., Vlahavas, I.: MULAN: a java library for multi-label learning. J. Mach. Learn. Res.12, 2411–2414 (2011)

    MathSciNet MATH  Google Scholar 

56. Turnbull, D., Barrington, L., Torres, D., Lanckriet, G.: Semantic annotation and retrieval of music and sound effects. IEEE Trans. Audio Speech Lang. Process.16(2), 467–476 (2008)

    Article  Google Scholar 

57. Vembu, S., Gärtner, T.: Label ranking algorithms: a survey. In: Preference learning, pp. 45–64. Springer (2011)

    Google Scholar 

58. Wieczorkowska, A., Synak, P., Raś, Z.: Multi-label classification of emotions in music. In: Intelligent Information Processing and Web Mining. AISC, vol. 35, Chap. 30, pp. 307–315 (2006)

    Google Scholar 

59. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput.1(1), 67–82 (1997)

    Article  Google Scholar 

60. Zhang, M., Zhang, K.: Multi-label learning by exploiting label dependency. In: Proceedings of 16th International Conference on Knowledge Discovery and Data Mining, ACM SIGKDD’10, pp. 999–1008 (2010)

    Google Scholar 

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