Multi-label learning: A review of the state of the art and ongoing research

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Volumn 4, Issue 6, 2014, Pages 411-444

  Gibaja, Eva ^a   Ventura, Sebastián ^a,b  

^a UNIVERSITY OF CÓRDOBA   (Spain)

^b KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING;

FORMAL DEFINITION; IMPROVE PERFORMANCE; MULTI-LABEL LEARNING; STATE OF THE ART;

LEARNING SYSTEMS;

EID: 84912136436     PISSN: 19424787     EISSN: 19424795     Source Type: Journal    
DOI: 10.1002/widm.1139     Document Type: Article

References (270)

1. Zhang ML, Zhou ZH. A Review On Multi-Label Learning Algorithms. IEEE Trans Knowl Data Eng 2014, 26:1819-1837.
2. First International Workshop on Learning from Multi-Label Data (MLD'09). Available at: http://lpis.csd.auth.gr/workshops/mld09/mld09.pdf. (2009).
3. Second International Workshop on Learning from Multi-Label Data (MLD'10). http://cse.seu.edu.cn/conf/MLD10/files/MLD'10.pdf (2010).
4. Extreme Classification: Multi-Class & Multi-Label Learning with Millions of Categories. Available at: http://nips.cc/Conferences/2013/Program/event.php?ID=3707 (2013).
5. Special issue on learning from multi-label data. Mach Learn 2012, 88.
6. LAMDA: Learning and Mining from Data. Data & Code. Available at: http://lamda.nju.edu.cn/Data.ashx.
7. Chang CC, Lin CJ. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2, 27:1-27:27 (2011). Available at: http://www.csie.ntu.edu.tw/cjlin/libsvm.
8. Read J. MEKA: a multi-label extension to WEKA. Available at: http://meka.sourceforge.net/. (2012).
9. Tsoumakas G, Spyromitros-Xioufis E, Vilcek J, Vlahavas I. Mulan: a java library for multi-label learning. J Mach Learn Res 2011, 12:2411-2414.
10. de Carvalho A, Freitas A. A tutorial on multi-label classification techniques. In: Foundations of Computational Intelligence, vol. 5, Berlin/Heidelberg: Springer; 2009, 177-195.
11. Tsoumakas G, Katakis I. Multi label classification: an overview. Int J Data Warehousing Min 2007, 3:1-13.
12. Tsoumakas G, Katakis I, Vlahavas I. Mining multi-label data. In: Data Mining and Knowledge Discovery Handbook, Part 6. Springer; 2010, 667-685.
13. Madjarov G, Kocev D, Gjorgjevikj D, Džeroski S. An extensive experimental comparison of methods for multi-label learning. Pattern Recogn 2012, 45:3084-3104.
14. Loza E, Fürnkranz J. Efficient pairwise multilabel classification for large-scale problems in the legal domain. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD-2008), Springer-Verlag; 2008, 50-65.
15. Loza E, Fürnkranz J. Efficient multilabel classification algorithms for large-scale problems in the legal domain. In: Semantic Processing of Legal Texts, Lecture Notes in Computer Science, vol. 6036, Berlin/Heidelberg: Springer; 2010, 192-215.
16. Rubin T, Chambers A, Smyth P, Steyvers M. Statistical topic models for multi-label document classification. Mach Learn 2012, 88:157-208.
17. Ueda N, Saito K. Parametric mixture models for multi-labeled text. In: Proceedings on Neural Information Processing Systems (NIPS); 2002, 721-728.
18. Schapire RE, Singer Y. BoosTexter: a boosting-based system for text categorization. Mach Learn 2000, 39:135-168.
19. Nguyen CD, Dung TA, Cao TH. Text classification for DAG-structured categories. In: Advances in Knowledge Discovery and Data Mining, Lecture Notes in Computer Science, vol. 3518, chap. 36, Berlin/Heidelberg: Springer; 2005; 1-18.
20. Spat S, Cadonna B, Rakovac I, Gütl C, Leitner H, Stark G, Beck P. Enhanced information retrieval from narrative german-language clinical text documents using automated document classification. In: eHealth Beyond the Horizon-Get IT There, Proceedings of MIE2008, The XXIst International Congress of the European Federation for Medical Informatics, Göteborg, Sweden; 2008, 473-478.
21. Cong H, Tong LH. Grouping of TRIZ inventive principles to facilitate automatic patent classification. Expert Systems with Applications 2008, 34:788-795.
22. Oza N, Castle JP, Stutz J. Classification of aeronautics system health and safety documents. IEEE Trans Syst Man Cybern C Appl Rev 2009, 39:670-680.
23. Lauser B, Hotho A. Automatic multi-label subject indexing in a multilingual environment. In: European Conference on Digital Libraries (ECDL), Lecture Notes in Computer Science, vol. 2769; 2003, 140-151.
24. Katakis I, Tsoumakas G, Vlahavas I. Multilabel text classification for automated tag suggestion. In: Proceedings of the ECML/PKDD 2008 Discovery Challenge; 2008.
25. Song Y, Zhang L, Giles CL. Automatic tag recommendation algorithms for social recommender systems. ACM Trans Web 2011, 5:1-31.
26. Yearwood J, Mammadov M, Banerjee A. Profiling phishing emails based on hyperlink information. In: International Conference on Advances in Social Networks Analysis and Mining; 2010, 120-127.
27. Yan Y, Fung G, Dy JG, Rosales R. Medical coding classification by leveraging inter-code relationships. In: Proceedings of the 16th International Conference on Knowledge Discovery and Data Mining (KDD '10), New York, NY, USA; 2010, 193-202.
28. Tang L, Rajan S, Narayanan VK. Large scale multi-label classification via metalabeler. In: Proceedings of the 18th International Conference on World Wide Web (WWW '09), New York, NY, USA; 2009, 211-220.
29. Bhowmick PK, Basu A, Mitra P. Reader perspective emotion analysis in text through ensemble based multi-label classification framework. Comput Inf Sci 2009, 2:64-74.
30. Bhowmick PK, Basu A, Mitra P, Prasad A. Sentence level news emotion analysis in fuzzy multi-label classification framework (special issue on natural language processing and its applications). Res Comput Sci 2010, 46:143-154.
31. Nasierding G, Kouzani A. Image to text translation by multi-label classification. In: Advanced Intelligent Computing Theories and Applications with Aspects of Artificial Intelligence, Lecture Notes in Computer Science, vol. 6216, Berlin/Heidelberg: Springer; 2010, 247-254.
32. Wang M, Zhou X, Chua TS. Automatic image annotation via local multi-label classification. In: Proceedings of the 2008 International Conference on Content-Based Image and Video Retrieval (CIVR '08), New York, NY, USA; 2008, 17-26.
33. Kumar N, Berg AC, Belhumeur PN, Nayar SK. Attribute and simile classifiers for face verification. In: IEEE International Conference on Computer Vision (ICCV); 2009.
34. Wang J, Zhao Y, Wu X, Hua XS. A transductive multi-label learning approach for video concept detection. Pattern Recogn 2010, 44:2274-2286.
35. Shotton J, Winn J, Rother C, Criminisi A. TextonBoost for image understanding: multi-class object recognition and segmentation by jointly modeling texture, layout, and context. Int J Comput Vision 2009, 81:2-23.
36. Ma A, Sethi I, Patel N. Multimedia content tagging using multilabel decision tree. In: 11th IEEE International Symposium on Multimedia (ISM '09); 2009, 606-611.
37. Trohidis K, Tsoumakas G, Kalliris G, Vlahavas I. Multi-label classification of music into emotions. In: Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR 2008); 2008, 325-330.
38. Pachet F, Roy P. Improving multilabel analysis of music titles: a large-scale validation of the correction approach. IEEE Trans Audio Speech Lang Proc 2009, 17:335-343.
39. 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 2010, 32:1284-1297.
40. Barutcuoglu Z, Schapire RE, Troyanskaya OG. Hierarchical multi-label prediction of gene function. Bioinformatics 2006, 22:830-836.
41. Cesa-Bianchi N, Re M, Valentini G. Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference. Mach Learn 2012, 88:209-241.
42. Cesa-Bianchi N, Valentini G. Hierarchical cost-sensitive algorithms for genome-wide gene function prediction. J Mach Learn Res 2010, 8:14-29.
43. Cheng W, Dembczyński K, Hüllermeier E. Graded multilabel classification: the ordinal case. In: Proceedings of the 27 th International Conference on Machine Learning (ICML); 2010, 223-230.
44. Elisseeff A, Weston J. A kernel method for multi-labelled classification. In: Advances in Neural Information Processing Systems (NIPS), vol. 14; 2001, 681-687.
45. Valentini G, Re M. Weighted true path rule: a multilabel hierarchical algorithm for gene function prediction. In: Proceedings of the 1st International Workshop on Learning from Multi-Label Data (MLD-ECML 2009), Bled, Slovenia; 2009, 132-145.
46. Zhang ML, Zhou ZH. Multilabel neural networks with applications to functional genomics and text categorization. IEEE Trans Knowl Data Eng 2006, 18:1338-1351.
47. Alves RT, Delgado MR, Freitas AA. Multi-label hierarchical classification of protein functions with artificial immune systems. In: Proceedings of the Brazilian Symposium in Bioinformatics (BSB-2008), Lecture Notes in Bioinformatics, vol. 5167; 2008, 1-12.
48. Alves RT, Delgado MR, Freitas AA. Knowledge discovery with artificial immune systems for hierarchical multi-label classification of protein functions. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Barcelona, Spain; 2010, 1-8.
49. Chan A, Freitas AA. A new ant colony algorithm for multi-label classification with applications in bioinfomatics. In: GECCO '06: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, New York, USA; 2006, 27-34.
50. Otero F, Freitas A, Johnson C. A hierarchical multi-label classification ant colony algorithm for protein function prediction. Memetic Comput 2010, 2:165-181.
51. Duwairi R, Kassawneh A. A framework for predicting proteins 3D structures. In: IEEE/ACS International Conference on Computer Systems and Applications (AICCSA '08), Washington, DC, USA; 2008, 37-44.
52. Chou KC, Wu ZC, Xiao X. iLoc-Euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins. PLoS ONE 2011, 6.
53. Mammadov MA, Rubinov AM, Yearwood J. The study of drug-reaction relationships using global optimization techniques. Optim Method Softw 2007, 22:99-126.
54. Kawai K, Takahashi Y. Identification of the dual action antihypertensive drugs using TFS-based support vector machines. Chem-Bio Inf J 2009, 4:44-51.
55. Ukwatta E, Samarabandu J. Vision based metal spectral analysis using multi-label classification. In: Canadian Conference on Computer and Robot Vision (CRV '09); 2009, 132-139.
56. Tang L, Liu H. Relational learning via latent social dimensions. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '09), New York, NY, USA; 2009, 817-826.
57. Tang L, Liu H. Scalable learning of collective behavior based on sparse social dimensions. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management (CIKM '09), New York, NY, USA; 2009, 1107-1116.
58. Krohn-Grimberghe A, Drumond L, Freudenthaler C, Schmidt-Thieme L. Multi-relational matrix factorization using Bayesian personalized ranking for social network data. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining (WSDM '12), New York, NY, USA; 2012, 173-182.
59. Peters S, Denoyer L, Gallinari P. Iterative annotation of multi-relational social networks. In: International Conference on Advances in Social Networks Analysis and Mining (ASONAM); 2010, 96-103.
60. Özpolat E, Akar GB. Automatic detection of learning styles for an e-learning system. Comput Educ 2009, 53:355-367.
61. López VF, de la Prieta F, Ogihara M, Wong DD. A model for multi-label classification and ranking of learning objects. Expert Syst Appl 2012, 39:8878-8884.
62. Zhang Y, Burer S, Street WN, Bennett K, Parrado-hern E. Ensemble pruning via semi-definite programming. J Mach Learn Res 2006, 7:1315-1338.
63. Shao H, Li G, Liu G, Wang Y. Symptom selection for multi-label data of inquiry diagnosis in traditional Chinese medicine. Sci China Ser F-Info Sci 2010, 1:1-13.
64. Abbas Q, Celebi M, Serrano C, García IF, Ma G. Pattern classification of dermoscopy images: a perceptually uniform model. Pattern Recogn 2013, 46:86-97.
65. Zhang ML, Zhou ZH. A k-nearest neighbor based algorithm for multi-label classification. In: Proceedings of the IEEE International Conference on Granular Computing (GrC), Beijing, China; 2005, 718-721.
66. Fan RE, Lin CJ. A study on threshold selection for multi-label classification. Technical Report, National Taiwan University; 2007.
67. Ioannou M, Sakkas G, Tsoumakas G, Vlahavas IP. Obtaining bipartitions from score vectors for multi-label classification. In: 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI); 2010, 409-416.
68. Montejo-Ráez A, Ureña López L. Selection strategies for multi-label text categorization.In: Advances in Natural Language Processing, Lecture Notes in Computer Science, vol. 4139; 2006, 585-592.
69. Yang Y. A study of thresholding strategies for text categorization. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '01), New York, NY, USA; 2001, 137-145.
70. Bielza C, Li G, Larrañaga P. Multi-dimensional classification with Bayesian networks. Int J Approx Reasoning 2011, 52:705-727.
71. Liu G, Lin Z, Yu Y. Multi-output regression on the output manifold. Pattern Recogn 2009, 42:2737-2743.
72. Ghamrawi N, McCallum A. Collective multi-label classification. In: CIKM '05: Proceedings of the 14th ACM International Conference on Information and Knowledge Management; 2005, 195-200.
73. Dembczyński K, Waegeman W, Cheng W, Hüllermeier E. Regret analysis for performance metrics in multi-label classification: the case of hamming and subset zero-one loss. In: Machine Learning and Knowledge Discovery in Databases, Lecture Notes in Computer Science, vol. 6321. Berlin/Heidelberg: Springer; 2010, 280-295.
74. Schapire RE, Singer Y. Improved boosting algorithms using confidence-rated predictions. Mach Learn 1999, 37:297-336.
75. Godbole S, Sarawagi S. Discriminative methods for multi-labeled classification. In: Proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining; 2004, 22-30.
76. Crammer K, Singer Y. A family of additive online algorithms for category ranking. J Mach Learn Res 2003, 3:1025-1058.
77. Park SH, Fürnkranz J. Multi-label classification with label constraints. Technical Report, TUD-KE-2008-04, Knowledge Engineering Group, TU Darmstadt; 2008. Available at: http://www.ke.tu-darmstadt.de/publications/reports/tud-ke-2008-04.pdf.
78. Read J. Scalable multi-label classification. PhD Thesis, University of Waikato, 2010.
79. Bianchi NC, Gentile C, Zaniboni L. Incremental algorithms for hierarchical classification. J Mach Learn Res 2006, 7:31-54.
80. Menca EL. Multi-label classification in parallel tasks. In: 2nd International Workshop on Learning from Multi-Label Data (MLD'10); 2010, 29-36.
81. Nguyen N, Caruana R. Classification with partial labels. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '08), New York, NY, USA; 2008, 551-559.
82. Zafra A, Gibaja E, Ventura S. Multiple instance learning with multiple objective genetic programming for web mining. Appl Soft Comput 2011, 11:93-102.
83. Petterson J, Caetano TS. Reverse multi-label learning. In: Advances in Neural Information Processing Systems (NIPS); 2010, 1912-1920.
84. Aiolli F, Cardin R, Sebastiani F, Sperduti A. Preferential text classification: learning algorithms and evaluation measures. Inf Retr 2009, 12:559-580.
85. Bucak S, Jin R, Jain A. Multi-label learning with incomplete class assignments. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2011, 2801 -2808.
86. Sun YY, Zhang Y, Zhou ZH. Multi-label learning with weak label. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence; 2010, 593-598.
87. Li H, Guo YJ, Wu M, Li P, Xiang Y. Combine multi-valued attribute decomposition with multi-label learning. Expert Syst Appl 2010, 37:8721-8728.
88. Blum A, Mitchell T. Combining labeled and unlabeled data with co-training. In: 11th Annual Conference on Computational Learning Theory, Madison, WI; 1998, 92-100.
89. Lewis DD, Yang Y, Rose TG, Li F. RCV1: a new benchmark collection for text categorization research. J Mach Learn Res 2005, 5:361-397.
90. Boutell M, Luo J, Shen X, Brown C. Learning multi-label scene classification. Pattern Recogn 2004, 37:1757-1771.
91. Zhang ML, Zhou ZH. Multi-label learning by instance differentiation. In: AAAI Conference on Artificial Intelligence, 2007, 669-674.
92. Huang GB, Ding X, Zhou H. Optimization method based extreme learning machine for classification. Neurocomputing 2010, 74:155-163.
93. Xiao X, Wu ZC, Chou KC. iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites. J Theor Biol 2011, 284:42-51.
94. Vens C, Struyf J, Schietgat L, Džeroski S, Blockeel H. Decision trees for hierarchical multi-label classification. Mach Learn 2008, 73:185-214.
95. Schapire RE, Singer Y. Improved boosting algorithms using confidence-rated predictions. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory (COLT' 98), New York, NY, USA; 1998, 80-91.
96. Turnbull D, Barrington L, Torres D, Lanckriet G. Semantic annotation and retrieval of music and sound effects. IEEE Trans Audio Speech Lang Proc 2008, 16:467-476.
97. Read J, Pfahringer B, Holmes G, Frank E. Classifier chains for multi-label classification. Mach Learn 2011, 85:1-27.
98. Dembczyński K, Cheng W, Hüllermeier E. Bayes optimal multilabel classification via probabilistic classifier chains. In: Proceedings of the 27th International Conference on Machine Learning (ICML); 2010, 279-286.
99. Antenreiter M, Ortner R, Auer P. Combining classifiers for improved multilabel image classification. In: Proceedings of the 1st Workshop on Learning from Multilabel Data (MLD) Held in Conjunction with ECML/PKDD, Bled, Slovenia; 2009, 16-27.
100. Tsoumakas G, Dimou A, Spyromitros E, Mezaris V, Kompatsiaris I, Vlahavas I. Correlation-based pruning of stacked binary relevance models for multi-label learning. In: Proceedings of the 1st International Workshop on Learning from Multi-Label Data (MLD'09), Bled, Slovenia; 2009, 101-116.
101. Cherman EA, Metz J, Monard MC. Incorporating label dependency into the binary relevance framework for multi-label classification. Expert Syst Appl 2012, 39:1647-1655.
102. Hüllermeier E, Fürnkranz J, Cheng W, Brinker K. Label ranking by learning pairwise preferences. Artif Intell 2008, 172:1897-1916.
103. Brinker K, Fürnkranz J, Hüllermeier E. A unified model for multilabel classification and ranking. In: Proceeding of the ECAI 2006: 17th European Conference on Artificial Intelligence; 2006, 489-493.
104. Fürnkranz J, Hüllermeier E, Lozamenca E, Brinke K. Multilabel classification via calibrated label ranking. Mach Learn 2008, 73:133-153.
105. Loza E, Park SH, Fürnkranz J. Efficient voting prediction for pairwise multilabel classification. Neurocomputing 2010, 73:1164-1176.
106. Madjarov G, Gjorgjevikj D, Džeroski S. Dual layer voting method for efficient multi-label classification. In: Proceedings of the 5th Iberian Conference on Pattern Recognition and Image Analysis, Lecture Notes in Computer Science, vol. 6669; 2011, 232-239.
107. Read J. A pruned problem transformation method for multi-label classification. In: Proceedings of the NZ Computer Science Research Student Conference; 2008, 143-150.
108. Tenenboim L, Rokach L, Shapira B. Identification of label dependencies for multi-label classification. In: 2nd International Workshop on Learning from Multi-Label Data (MLD'10); 2010, 53-60.
109. Tsoumakas G, Katakis I, Vlahavas I. Random k-Labelsets for Multi-Label Classification. IEEE Trans Knowl Data Eng 2010, 23:1079-1089.
110. Lo H, Wang J, Wang H, Lin S. Cost-sensitive multi-label learning for audio tag annotation and retrieval. IEEE Trans Multimedia 2011, 13:518-529.
111. Rokach L, Schclar A, Itach E. Ensemble methods for multi-label classification. Expert Syst Appl 2014, 41:7507-7523.
112. Kocev D, Vens C, Struyf J, Džeroski S. Ensembles of multi-objective decision trees. In: Proceedings of the 18th European Conference on Machine Learning (ECML '07), Berlin/Heidelberg: Springer; 2007, 624-631.
113. Nasierding G, Kouzani AZ, Tsoumakas G. A triple-random ensemble classification method for mining multi-label data. In: Proceedings of the 2010 IEEE International Conference on Data Mining Workshops (ICDMW '10), Washington, DC, USA; 2010, 49-56.
114. Sarinnapakorn K, Kubat M. Induction from multi-label examples in information retrieval systems: a case study. Appl Artif Intell 2008, 22:407-432.
115. Xu J. Constructing a fast algorithm for multi-label classification with support vector data description. In: Proceedings of the IEEE International Conference on Granular Computing (GrC); 2010, 817-821.
116. Clare A, King RD. Knowledge discovery in multi-label phenotype data. In: PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery, Lecture Notes in Computer Science, vol. 2168; 2001; 42-53.
117. Noh HG, Song MS, Park SH. An unbiased method for constructing multilabel classification trees. Comput Stat Data Anal 2004, 47:149-164.
118. Blockeel H, Raedt LD, Ramon J. Top-down induction of clustering trees. In: Proceedings of the Fifteenth International Conference on Machine Learning (ICML '98), San Francisco, CA, USA; 1998, 55-63.
119. Petrovskiy M. Paired comparisons method for solving multi-label learning problem. In: Sixth International Conference on Hybrid Intelligent Systems (HIS '06); 2006, 42.
120. Xu J. An efficient multi-label support vector machine with a zero label. Expert Syst Appl 2012, 39:4796-4804.
121. Wang L, Chang M, Feng J. Parallel and sequential support vectormachines for multi-label classification. Int J Inf Technol 2005, 11:11-18.
122. Wan SP, Xu JH. A multi-label classification algorithm based on triple class support vector machine. In: International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR '07), vol. 4; 2007, 1447-1452.
123. Li J, Xu J. A fast multi-label classification algorithm based on double label support vector machine. In: Proceedings of the 2009 International Conference on Computational Intelligence and Security (CIS '09), Washington, DC, USA; 2009, 30-35.
124. Jiang A, Wang C, Zhu Y. Calibrated Rank-SVM for multi-label image categorization. In: IEEE World Congress on Computational Intelligence (IJCNN); 2008, 1450-1455.
125. Cheng W, Hüllermeier E. Combining instance-based learning and logistic regression for multilabel classification. Mach Learn 2009, 76:211-225.
126. Spyromitros E, Tsoumakas G, Vlahavas I. An empirical study of lazy multilabel classification algorithms. In: SETN '08: Proceedings of the 5th Hellenic Conference on Artificial Intelligence, Berlin, Heidelberg; 2008, 401-406.
127. Younes Z, Aballah F, Denoeux T. Multi-label classification algorithm derived from k-nearest neighbor rule with label dependencies. In: Proceedings of the 16th European Signal Processing Conference; 2008.
128. Brinker K, Hüllermeier E. Case-based multilabel ranking. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI'07), San Francisco, CA, USA; 2007, 702-707.
129. Younes Z, Abdallah F, Denoux T. Fuzzy multi-label learning under veristic variables. In: IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2010, 1-8.
130. Younes Z, Abdallah F, Denoeux T. Evidential multi-label classification approach to learning from data with imprecise labels. In: Computational Intelligence for Knowledge-Based Systems Design, Lecture Notes in Computer Science, vol. 6178, Berlin/Heidelberg: Springer; 2010, 119-128.
131. Jiang JY, Tsai SC, Lee SJ. FSKNN: multi-label text categorization based on fuzzy similarity and k nearest neighbors. Expert Syst Appl 2012, 39:2813-2821.
132. Lin X, Chen XW. Mr.KNN: soft relevance for multi-label classification. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM '10), New York, NY, USA; 2010, 349-358.
133. Zhang ML. Ml-rbf: RBF neural networks for multi-label learning. Neural Proc Lett 2009, 29:61-74.
134. Ciarelli PM, Oliveira E, Badue C, Souza AF. Multi-label text categorization using a probabilistic neural network. Int J Comput Inf Syst Ind Manage Appl 2009, 1:133-144.
135. Ciarelli P, Oliveira E. An enhanced probabilistic neural network approach applied to text classification. In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Lecture Notes in Computer Science, vol. 5856, chap. 78. Berlin/Heidelberg: Springer; 2009, 661-668.
136. Sapozhnikova E. ART-based neural networks for multi-label classification. In: Advances in Intelligent Data Analysis VIII, Lecture Notes in Computer Science, vol. 5772, Berlin/Heidelberg: Springer; 2009, 167-177.
137. McCallum AK. Multi-label text classification with a mixture model trained by EM. In: AAAI 99 Workshop on Text Learning; 1999.
138. Kaneda Y, Ueda N, Saito K. Extended parametric mixture model for robust multi-labeled text categorization. In: Knowledge-Based Intelligent Information and Engineering Systems, Lecture Notes in Computer Science, vol. 3214; 2004, 616-623.
139. Wang H, Huang M, Zhu X. A generative probabilistic model for multi-label classification. In: ICDM '08: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Washington, DC, USA; 2008, 628-637.
140. Streich A, Buhmann J. Classification of multi-labeled data: a generative approach. In: Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD '08), Springer-Verlag; 2008, 390-405.
141. Thabtah FA, Cowling P, Peng Y, Rastogi R, Morik K, Bramer M, Wu X. MMAC: a new multi-class, multi-label associative classification approach. In: Proceedings of the Fourth IEEE International Conference on Data Mining, ICDM 2004; 2004, 217-224.
142. Thabtah FA, Cowling PI. A greedy classification algorithm based on association rule. Appl Soft Comput 2007, 7:1102-1111.
143. Rak R, Kurgan L, Reformat M. A tree-projection-based algorithm for multi-label recurrent-item associative-classification rule generation. Data Knowl Eng 2008, 64:171-197.
144. Veloso A, Meira W Jr, Gonçalves MA, Zaki MJ. Multi-label lazy associative classification. In: Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), Warsaw, Poland; 2007, 605-612.
145. Cano A, Zafra A, Galindo ELG, Ventura S. A grammar-guided genetic programming algorithm for multi-label classification. In: 16th European Conference, EuroGP, Lecture Notes in Computer Science, vol. 7831; 2013, 217-228.
146. Ávila J, Gibaja E, Ventura S. Evolving multi-label classification rules with gene expression programming: a preliminary study. In: Hybrid Artificial Intelligence Systems (HAIS), Lecture Notes in Computer Science, vol. 6077; 2010, 9-16.
147. Ávila JL, Gibaja EL, Zafra A, Ventura S. A gene expression programming algorithm for multi-label classification. J Mult-Valued Log S 2011, 17:183-206.
148. Xu H, Xu J. Designing a multi-label kernel machine with two-objective optimization. In: Proceedings of the 2010 International Conference on Artificial Intelligence and Computational Intelligence (AICI'10): Part I, Berlin, Heidelberg; 2010, 282-291.
149. Shi C, Kong X, Yu P, Wang B. Multi-objective multi-label classification. In: Proceedings of the SIAM International Conference on Data Mining, Anaheim, CA, USA; 2012, 355-366.
150. Shi C, Kong X, Yu PS, Wang B. Multi-label ensemble learning. In: European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD); 2011, 223-239.
151. Gonçalves EC, Plastino A, Freitas AA. A genetic algorithm for optimizing the label ordering in multi-label classifier chains. In: IEEE 25th International Conference on Tools with Artificial Intelligence (ICTAI); 2013, 469-476.
152. Sebastiani F, Sperduti A, Valdambrini N. An improved boosting algorithm and its application to text categorization. In: Proceedings of the Ninth International Conference on Information and Knowledge Management (CIKM '00), New York, NY, USA; 2000, 78-85.
153. Nardiello P, Sebastiani F, Sperduti A. Discretizing continuous attributes in adaboost for text categorization. In: Advances in Information Retrieval, Lecture Notes in Computer Science, vol. 2633, Berlin/Heidelberg: Springer; 2003, 320-334.
154. De Comité F, Gilleron R, Tommasi M. Learning multi-label alternating decision trees from texts and data. In: Proceedings of the 3rd International Conference on Machine Learning and Data Mining in Pattern Recognition (MLDM'03), Berlin/Heidelberg: Springer; 2003, 35-49.
155. Diao L, Hu K, Lu Y, Shi C. Boosting simple decision trees with Bayesian learning for text categorization. In: Proceedings of the 4th World Congress on Intelligent and Automation, Shanghai, China; 2002, 321-325.
156. Johnson M, Cipolla R. Improved image annotation and labelling through multi-label boosting. In: British Machine Vision Association (BMVC); 2005.
157. Esuli A, Fagni T, Sebastiani F. MP-Boost: a multiple-pivot boosting algorithm and its application to text categorization. In: String Processing and Information Retrieval (SPIRE), Lecture Notes in Computer Science, vol. 4209. Berlin/Heidelberg: Springer; 2006, 1-12.
158. Yan R, Tesic J, Smith JR. Model-shared subspace boosting for multi-label classification. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '07), New York, NY, USA; 2007, 834-843.
159. Zhang X, Yuan Q, Zhao S, Fan W, Zheng W, Wang Z. Multi-label classification without the multi-label cost. In: Proceedings of the 10th SIAM International Conference on Data Mining; 2010.
160. Zhang Y, Schneider J. Multi-label output codes using canonical correlation analysis. In: Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS); 2011, 873-882.
161. Kouzani A. Multilabel classification using error correction codes. In: Advances in Computation and Intelligence, Lecture Notes in Computer Science, vol. 6382; 2010, 444-454.
162. Kajdanowicz T, Wozniak M, Kazienko P. Multiple classifier method for structured output prediction based on error correcting output codes. In: Intelligent Information and Database Systems, Lecture Notes in Computer Science, vol. 6592; 2011, 333-342.
163. Vateekul P, Kubat M. Fast induction of multiple decision trees in text categorization from large scale, imbalanced, and multi-label data. In: Proccedings of the 2009 IEEE International Conference on Data Mining Workshops (ICDMW); 2009, 320-325.
164. Zhou T, Tao D, Wu X. Compressed labeling on distilled labelsets for multi-label learning. Mach Learn 2012, 88:69-126.
165. Wolpert DH. Stacked generalization. Neural Networks 1992, 5:241-259.
166. Rokach L, Itach E. An ensemble method for multi-label classification using a transportation model. In: Proceedings of the 1st Workshop on Learning from Multilabel Data (MLD) Held in Conjunction with ECML/PKDD, Bled, Slovenia; 2009, 49-60.
167. Rokach L, Itach E. An ensemble method for multi-label classification using an approximation algorithm for the set covering problem. In: Proceedings of the 2nd International Workshop on Learning from Multilabel Data (MLD), Haifa, Israel; 2010, 37-44.
168. Tax D, Duan RPW. Support vector data description. Mach Learn 2004, 54:45-66.
169. Shafer G. A Mathematical Theory of Evidence. Princeton University Press; 1976.
170. Nettleton D, Banerjee T. Testing the equality of distributions of random vectors with categorical components. Comput Stat Data Anal 2001, 37:195-208.
171. Gonçalves T, Quaresma P. A preliminary approach to the multilabel classification problem of portuguese juridical documents. Prog Artif Intell, Lect Notes Comput Sci 2003, 2902:435-444.
172. Gonçalves T, Quaresma P. The impact of NLP techniques in the multilabel text classification problem. In: Proceedings of Intelligent Information Processing and Web Mining (IIPWM'04), Advances in Soft Computing; 2004, 424-428.
173. Li X, Wang L, Sung E. Multilabel SVM active learning for image classification. In: International Conference on Image Processing (ICIP '04); 2004, 2207-2210.
174. Rao P, Kupper L. Ties in paired-comparison experiments: a generalization of the bradley-terry model. Am Stat Assoc 1967, 62:194-204.
175. Keller JM, Gray MR, Givens JA. Fuzzy K-Nearest neighbor algorithm. IEEE Trans Syst Man Cybern 1985, 15:580-585.
176. Yager RR. Veristic variables. IEEE Trans Syst Man Cybern 2000, 30:71-84.
177. Specht DF. Probabilistic neural networks. Neural Netw 1990, 3:109-118.
178. Dempster AP, Laird NM. Rubin., D.B.: Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B 1977, 39:1-38.
179. Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. J Mach Learn Res 2003, 3.
180. Lafferty J, McCallum A, Pereira F. Conditional random fields: probabilistic models for segmenting and labelling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning (ICML); 2001, 282-289.
181. Wang X, Liu X, Shi Z, Shi Z, Sui H. Voting conditional random fields for multi-label image classification. In: 3rd International Congress on Image and Signal Processing (CISP); 2010, 1984-1988.
182. Agarwal R, Aggarwal C, Prasad V. A tree projection algorithm for generation of frequent item sets. J Parallel Distr Com 2001, 61:350-371.
183. Parpinelli R, Lopes H, Freitas A. Data mining with an ant colony optimization algorithm. IEEE Trans Evol Comput 2002, 6:321-332.
184. Ferreira C. Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 2001, 13:87-129.
185. Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 2002, 3:182-197.
186. Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 1997, 55:119-139.
187. Freund Y, Mason L. The alternating decision tree learning algorithm. In: Proceedings of the Sixteenth International Conference on Machine Learning (ICML '99), San Francisco, CA, USA; 1999, 124-133.
188. Fan W, Wang H, Yu PS, Ma S. Is random model better? On its accuracy and efficiency. In: Proceedings of the Third IEEE International Conference on Data Mining (ICDM '03); 2003.
189. Dietterich TG, Bakiri G. Solving multiclass learning problems via error-correcting output codes. J Artif Intell Res 1995, 2:263-286.
190. Ferng CS, Lin HT. Multi-label classification with error-correcting codes. J Mach Learn Res 2011, 20:281-295.
191. Kajdanowicz T, Kazienko P. Multi-label classification using error correcting output codes. Int J Appl Math Comput Sci 2012, 22:829-840.
192. Fürnkranz J, Park SH. Error-correcting output codes as a transformation from multi-class to multi-label prediction. In: Discovery Science, Lecture Notes in Computer Science, vol. 7569 Berlin/Heidelberg: Springer; 2012, 254-267.
193. Dembczyński K, Waegeman W, Cheng W, Hüllermeier E. On label dependence in multi-label classification. In: Proceedings of the 2nd International Workshop on Learning from Multi-Label Data (MLD'10); 2010, 5-12.
194. Hsu D, Kakade S, Langford J, Zhang T. Multi-label prediction via compressed sensing. In: Advances in Neural Information Processing Systems (NIPS); 2009, 772-780.
195. Read J, Bifet A, Holmes G, Pfahringer B. Scalable and efficient multi-label classification for evolving data streams. Mach Learn 2012, 88:243-272.
196. Zhang ML, Zhang K. Multi-label learning by exploiting label dependency. In: Proceedings of the 16th International Conference on Knowledge Discovery and Data Mining (KDD '10), New York, NY, USA; 2010, 999-1008.
197. Clare A, King RD. Predicting gene function in Saccharomyces cerevisiae. Bioinformatics 2003, 2:42-49.
198. Zhang ML, Zhou ZH. ML-KNN: A lazy learning approach to multi-label learning. Pattern Recogn 2007, 40:2038-2048.
199. Read J, Pfahringer B, Holmes G. Multi-label classification using ensembles of pruned sets. In: ICDM '08: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Washington, DC, USA; 2008, 995-1000.
200. Huang SJ, Yu Y, Zhou ZH. Multi-label hypothesis reuse. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD12), Beijing, China; 2012, 525-533.
201. Huang SJ, Zhou ZH. Multi-label learning by exploiting label correlations locally. In: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence; 2012.
202. Qi GJ, Hua XS, Rui Y, Tang J, Mei T, Zhang HJ. Correlative multi-label video annotation. In: Proceedings of the 15th International Conference on Multimedia, New York, NY, USA; 2007, 17-26.
203. Zhu S, Ji X, Xu W, Gong Y. Multi-labelled classification using maximum entropy method. In: SIGIR '05: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA; 2005, 274-281.
204. Qi GJ, Hua XS, Rui Y, Tang J, Zhang HJ. Two-dimensional active learning for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008); 2008, 1-8.
205. Ji S, Tang L, Yu S, Ye J. A shared-subspace learning framework for multi-label classification. ACM Trans Knowl Discov Data 2010, 4:1-29.
206. Borchani H, Bielza C, Toro C, Larrañaga P. Predicting human immunodeficiency virus inhibitors using multi-dimensional bayesian network classifiers. Artif Intell Med 2013, 57:219-229.
207. van der Gaag LC, de Waal PR. Multi-dimensional Bayesian network classifiers. In: Third European Workshop on Probabilistic Graphical Models; 2006, 107-114.
208. Sucar LE, Bielza C, Morales EF, Hernandez-Leal P, Zaragoza JH, Larrañaga P. Multi-label classification with bayesian network-based chain classifiers. Pattern Recogn Lett 2014, 41:14-22.
209. Yang Y, Pedersen JO. A comparative study on feature selection in text categorization. In: Proceedings of the Fourteenth International Conference on Machine Learning (ICML '97), San Francisco, CA, USA; 1997, 412-420.
210. Tsoumakas G, Katakis I, Vlahavas I. Effective and efficient multilabel classification in domains with large number of labels. In: Proceedings of the ECML/PKDD 2008 Workshop on Mining Multidimensional Data (MMD'08), 2008.
211. Doquire G, Verleysen M. Feature selection for multi-label classification problems. In: 11th International Work-Conference on Artificial Neural Networks (IWANN), Lecture Notes in Computer Science, vol. 6691; 2011, 9-16.
212. Zhang ML, Peña JM, Robles V. Feature selection for multi-label naive Bayes classification. Inform Sci 2009, 179:3218-3229.
213. Jolliffe I. Principal Component Analysis. Springer; 1986.
214. Fisher RA. The use of multiple measurements in taxonomic problems. Ann Eugen 1936, 7:179-188.
215. Wang H, Ding C, Huang H. Multi-label linear discriminant analysis. In: Computer Vision-ECCV 2010, Lecture Notes in Computer Science, vol. 6316, Berlin/Heidelberg: Springer; 2010, 126-139.
216. Park CH, Lee M. On applying linear discriminant analysis for multi-labeled problems. Pattern Recogn Lett 2008, 29:878-887.
217. Luo X, Heywood ZAN. Evaluation of two systems on multi-class multi-label document classification. In: Proceedings of the 15th International Symposium on Methodologies for Intelligent Systems; 2005, 161-169.
218. 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, New York, NY, USA; 2005, 258-265.
219. Zhang Y, Zhou ZH. Multilabel dimensionality reduction via dependence maximization. In: ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 4, paper 14; 2010.
220. Gretton A, Bousquet O, Smola A, Schölkopf B: Measuring statistical dependence with Hilbert-Schmidt norms. In: Proceedings of the 16th International Conference on Algorithmic Learning Theory (ALT'05); 2005, 63-77.
221. Zhang ML. LIFT: multi-label learning with label-specific features. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI); 2011, 1609-1614.
222. Tsoumakas G, Menca EL, Katakis I, Park S, Fürnkrnaz J. On the combination of two decompositive multi-label classification methods. In: Workshop on Preference Learning, ECML PKDD 09; 2009, 114-133.
223. Charte F, Rivera A, del Jesus M, Herrera F. Improving multi-label classifiers via label reduction with association rules. In: Hybrid Artificial Intelligent Systems, Lecture Notes in Computer Science, vol. 7209. Berlin/Heidelberg: Springer; 2012, 188-199.
224. Han J, Pei J, Yin Y, Mao R. Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min Knowl Discov 2004, 8:53-87.
225. Sun L, Ji S, Ye J. Canonical correlation analysis for multilabel classification: a least-squares formulation, extensions, and analysis. IEEE Trans Pattern Anal Mach Intell 2011, 33:194-200.
226. Tai F, Lin HT. Multi-label classification with principal label space transformation. In: 2nd International Workshop on Learning from Multi-Label Data (MLD'10); 2010; 45-52.
227. Agrawal R, Gupta A, Prabhu Y, Varma M. Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages. In: Proceedings of the 22nd International Conference on World Wide Web (WWW13); 2013, 13-24.
228. Dekel O, Shamir O. Multiclass-multilabel classification with more labels than examples. In: Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS); 2010, 137-144.
229. Zhou ZH, Zhang ML, Huang SJ, Li YF. Multi-instance multi-label learning. Artif Intell 2012, 176:2291-2320.
230. He J, Gu H, Wang Z. Multi-instance multi-label learning based on Gaussian process with application to visual mobile robot navigation. Inform Sci 2012, 190:162-177.
231. Zhou ZH, Zhang ML. Multi-instance multi-label learning with application to scene classification. In: NIPS; 2006, 1609-1616.
232. Xu X, Frank E. Logistic regression and boosting for labeled bags of instances. In: Advances in Knowledge Discovery and Data Mining, Lecture Notes in Computer Science, vol. 3056, Berlin/Heidelberg: Springer; 2004, 272-281.
233. Shen C, Jing L, Ng M. Sparse-MIML: a sparsity-based multi-instance multi-learning algorithm. In: Energy Minimization Methods in Computer Vision and Pattern Recognition, Lecture Notes in Computer Science, vol. 8081; 2013, 294-306.
234. Zhang ML. A k-nearest neighbor based multi-instance multi-label learning algorithm. In: Proceedings of the 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), vol. 2, Arras, France; 2010, 207-212.
235. Zhang ML, Wang ZJ. MIMLRBF: RBF neural networks for multi-instance multi-label learning. Neurocomputing 2009, 72:3951-3956.
236. Zhang ML, Zhou ZH. M3MIML: a maximum margin method for multi-instance multi-label learning. In: ICDM '08: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Washington, DC, USA; 2008, 688-697.
237. Yang SH, Zha H, Hu BG. Dirichlet-Bernoulli alignment: a generative model for multi-class multi-label multi-instance corpora. In: Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada; 2009, 2143-2150.
238. Liu Y, Jin R, Yang L. Semi-supervised multi-label learning by constrained non-negative matrix factorization. In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI06), vol. 1; 2006, 421-426.
239. Chen G, Song Y, Wang F, Zhang C. Semi-supervised multi-label learning by solving a sylvester equation. In: Proceedings of the SIAM International Conference on Data Mining (SDM); 2008, 410-419.
240. Hu DY, Reichel L. Krylov-subspace methods for the Sylvester equation. Linear Algebra Appl 1992, 172:283-313.
241. Zha ZJ, Mei T, Wang J, Wang Z, Hua XS. Graph-based semi-supervised learning with multiple labels (special issue on emerging techniques for multimedia content sharing, search and understanding). Journal of Visual Communication and Image Representation 2009, 20:97-103.
242. Ahmed MS, Khan L, Oza NC, Rajeswari M. Multi-label ASRS dataset classification using semi supervised subspace clustering. In: Proceedings of the 2010 Conference on Intelligent Data Understanding (CIDU); 2010, 285-299.
243. Wu L, Zhang ML. Multi-label classification with unlabeled data: an inductive approach. In: Proceedings of the 5th Asian Conference on Machine Learning (ACML'13), Canberra, Australia; 2013, 197-212.
244. Chapelle O, Sindhwaniand V, Keerthi SS. Optimization techniques for semisupervised support vector machines. J Mach Learn Res 2008, 9:203-233.
245. Brinker K. On active learning in multi-label classification. In: From Data and Information Analysis to Knowledge Engineering, Studies in Classification, Data Analysis, and Knowledge Organization. Berlin/Heidelberg: Springer; 2006, 206-213.
246. Qi GJ, Hua XS, Rui Y, Tang J, Zhang HJ. Two-dimensional multilabel active learning with an efficient online adaptation model for image classification. IEEE Trans Pattern Anal Mach Intell 2009, 31:1880-1897.
247. Zhang X, Cheng J, Xu C, Lu H, Ma S. Multi-view multi-label active learning for image classification. In: IEEE International Conference on Multimedia and Expo, 2009, 258-261.
248. Yang B, Sun JT, Wang T, Chen Z. Effective multi-label active learning for text classification. In: KDD '09: Proceedings of the 15th International Conference on Knowledge Discovery and Data Mining, New York, NY, USA; 2009, 917-926.
249. Esuli A, Sebastiani F. Active learning strategies for multi-label text classification. In: Advances in Information Retrieval, Lecture Notes in Computer Science, vol. 5478. Berlin/Heidelberg: Springer; 2009, 102-113.
250. Qu W, Zhang Y, Zhu J, Qiu Q. Mining multi-label concept-drifting data streams using dynamic classifier ensemble. In: Advances in Machine Learning, Lecture Notes in Computer Science, vol. 5828, Berlin/Heidelberg: Springer; 2009, 308-321.
251. Domingos P, Hulten G. Mining high-speed data streams. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '00), New York, NY, USA; 2000, 71-80.
252. Bifet A, Holmes G, Pfahringer B, Kirkby R, Gavaldà R. New ensemble methods for evolving data streams. In: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2009, 139-148.
253. Read J, Pfahringer B, Holmes G. Generating synthetic multi-label data streams. In: ECML/PKKD 2009 Workshop on Learning from Multi-label Data (MLD'09); 2009.
254. Xioufis ES, Spiliopoulou M, Tsoumakas G, Vlahavas IP. Dealing with concept drift and class imbalance in multi-label stream classification. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI), Barcelona, Spain; 2011, 1583-1588.
255. The Gene Ontology Consortium. Gene ontology: tool for the unification of biology. Nat Genet 2000, 25:25-29.
256. Cerri, R., Barros, R.C., de Carvalho, A.C.P.L.F. A genetic algorithm for Hierarchical Multi-Label Classification. In: Proceedings of the 27th Annual ACM Symposium on Applied Computing (SAC '12), New York, NY, USA; 2012, 250-255.
257. Kiritchenko S, Matwin S, Famili AF. Functional annotation of genes using hierarchical text categorization. In: Proceedings of the BioLINK SIG: Linking Literature, Information and Knowledge for Biology (held at ISMB-05); 2005.
258. Blockeel H, Schietgat L, Struyf J, Dzrěoski S, Clare A. Decision trees for hierarchical multilabel classification: a case study in functional genomics. In: 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), Lecture Notes in Computer Science, vol. 4213; 2006, 18-29.
259. Clare A. Machine learning and data mining for yeast functional genomics. PhD Thesis, University of Wales, 2003.
260. Cesa-Bianchi, N., Gentile, C., Zaniboni, L. Hierarchical classification: combining Bayes with SVM. In: Proceedings of the Twenty-Third International Conference on Machine Learning (ICML); 2006, 177-184.
261. Esuli A, Fagni T, Sebastiani F. TreeBoost.MH: a boosting algorithm for multi-label hierarchical text categorization. In: String Processing and Information Retrieval (SPIRE), Lecture Notes in Computer Science, vol. 4209. Berlin/Heidelberg: Springer; 2006, 13-24.
262. Brucker F, Benites F, Sapozhnikova E. Multi-label classification and extracting predicted class hierarchies. Pattern Recogn 2010, 44:724-738.
263. Charte F, Rivera A, del Jesus M, Herrera F. A first approach to deal with imbalance in multi-label datasets. In: HAIS 2013-LNAI 8073; 2013; 150-160.
264. 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. Berlin/Heidelberg: Springer; 2010, 40-52.
265. Tahir MA, Kittler J, Bouridane A. Multilabel classification using heterogeneous ensemble of multi-label classifiers. Pattern Recogn Lett 2012, 33:513-523.
266. Nasierding G, Kouzani AZ. Empirical study of multi-label classification methods for image annotation and retrieval. In: Digital Image Computing: Techniques and Applications; 2010, 617-622.
267. Dimou A, Tsoumakas G, Mezaris V, Kompatsiaris I, Vlahavas I. An empirical study of multi-label learning methods for video annotation. In: International Workshop on Content-Based Multimedia Indexing, IEEE Computer Society, Los Alamitos, CA, USA; 2009, 19-24.
268. Chekina L, Rokach L, Shapira B. Meta-learning for selecting a multi-label classification algorithm. In: IEEE 11th International Conference on Data Mining Workshops (ICDMW); 2011, 220-227.
269. Dembczyński K, Waegeman W, Cheng W, Hüllermeier E. On label dependence and loss minimization in multi-label classification. Mach Learn 2012, 88:5-45.
270. Read J. Advances in multi-label classification. Available at: http://users.ics.aalto.fi/jesse/talks/Charla-Malaga.pdf. (2011).