Abstract
Multilabel learning has become a relevant learning paradigm in the past years due to the increasing number of fields where it can be applied and also to the emerging number of techniques that are being developed. This article presents an up-to-date tutorial about multilabel learning that introduces the paradigm and describes the main contributions developed. Evaluation measures, fields of application, trending topics, and resources are also presented.
- Qaisar Abbas, M. E. Celebi, Carmen Serrano, Irene Fondón, and Guangzhi Ma. 2013. Pattern classification of dermoscopy images: A perceptually uniform model. Pattern Recognition 46, 1 (2013), 86--97. Google ScholarDigital Library
- Fabio Aiolli. 2004. Large Margin Multiclass Learning: Models and Algorithms. PhD Dissertation. Università degli Studi di Pisa.Google Scholar
- Martin Antenreiter, Ronald Ortner, and Peter Auer. 2009. 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. 16--27.Google Scholar
- J. L. Ávila. 2013. Modelos de aprendizaje basados en programación genética para clasificación multietiqueta. PhD Dissertation. University of Córdoba.Google Scholar
- Jose L. Ávila, Eva. Gibaja, and Sebastián Ventura. 2010. Evolving multi-label classification rules with gene expression programming: A preliminary study. In Hybrid Artificial Intelligence Systems. Lecture Notes in Computer Science, Vol. 6077. Springer, Berlin, Chapter 2, 9--16. Google ScholarDigital Library
- Jose L. Ávila, Eva Gibaja, Amelia Zafra, and Se Ventura. 2011. A gene expression programming algorithm for multi-label classification. Journal of Multiple-Valued Logic and Soft Computing 17, 2--3 (2011), 183--206.Google Scholar
- K. Bache and M. Lichman. 2013. UCI Machine Learning Repository. Retrieved from http://archive.ics.uci.edu/ml.Google Scholar
- Kobus Barnard, Pinar Duygulu, Nando de Freitas, David Forsyth, David Blei, and Michael I. Jordan. 2003. Matching words and pictures. Journal of Machine Learning Research 3 (2003), 1107--1135. Google ScholarDigital Library
- Zafer Barutcuoglu, Robert E. Schapire, and Olga G. Troyanskaya. 2006. Hierarchical multi-label prediction of gene function. Bioinformatics (Oxford, England) 22, 7 (April 2006), 830--836. Google ScholarDigital Library
- Plaban K. Bhowmick, Anupam Basu, Pabitra Mitra, and Abhisek Prasad. 2010. Sentence level news emotion analysis in fuzzy multi-label classification framework. Research in Computer Science, Special Issue: Natural Language Processing and Its Applications 46 (2010), 143--154.Google Scholar
- Hendrik Blockeel, Luc De Raedt, and Jan Ramon. 1998. Top-down induction of clustering trees. In Proceedings of the 15th International Conference on Machine Learning (ICML’98). Morgan Kaufmann, San Francisco, CA, 55--63. Google ScholarDigital Library
- Hendrik Blockeel, Leander Schietgat, Jan Struyf, Sašo Dzěroski, and Amanda Clare. 2006. Decision trees for hierarchical multilabel classification: A case study in functional genomics. In Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD (Lecture Notes in Computer Science), Vol. 4213. 18--29. Google ScholarDigital Library
- Matthew R. Boutell, Jiebo Luo, Xipeng Shen, and Christopher M. Brown. 2004. Learning multi-label scene classification. Pattern Recognition 37, 9 (Sept. 2004), 1757--1771.Google ScholarCross Ref
- Leo Breiman, Jerome Friedman, R. A. Olshen, and Charles J. Stone. 1984. Classification and Regression Trees. Wadsworth.Google Scholar
- Forrest Briggs, Raviv Raich, Konstantinos Eftaxias, Zhong Lei, and Yonghong Huang. 2013. The ninth annual MLSP competition: Overview. In Proceedings of the 2013 IEEE International Workshop on Machine Learning for Signal Processing.Google Scholar
- Klaus Brinker. 2006. On active learning in multi-label classification. In From Data and Information Analysis to Knowledge Engineering, Myra Spiliopoulou, Rudolf Kruse, Christian Borgelt, Andreas Nürnberger, and Wolfgang Gaul (Eds.). Springer, Berlin, 206--213.Google Scholar
- Klaus Brinker, Johannes Fürnkranz, and Eyke Hüllermeier. 2006. A unified model for multilabel classification and ranking. In Proceedings of the 17th European Conference on Artificial Intelligence. IOS Press, Amsterdam, The Netherlands, 489--493. Google ScholarDigital Library
- Lijuan Cai. 2008. Multilabel Classification over Category Taxonomies. PhD Dissertation. Brown University. Google ScholarDigital Library
- Nicolò Cesa-Bianchi, Claudio Gentile, and Luca Zaniboni. 2006. Hierarchical classification: Combining bayes with SVM. In Proceedings of the 23rd International Conference on Machine Learning (ICML’06). 177--184. Google ScholarDigital Library
- Allen Chan and Alex A. Freitas. 2006. A new ant colony algorithm for multi-label classification with applications in bioinfomatics. In Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (GECCO’06). ACM, New York, NY, 27--34. Google ScholarDigital Library
- Chih-Chung Chang and Chih-Jen Lin. 2011. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 3 (2011), 27:1--27:27. Google ScholarDigital Library
- Lena Chekina, Lior Rokach, and Bracha Shapira. 2011. Meta-learning for selecting a multi-label classification algorithm. In Proceedings of the 11th International Conference on Data Mining Workshops (ICDMW’11). 220--227. Google ScholarDigital Library
- Weizhu Chen, Jun Yan, Benyu Zhang, Zheng Chen, and Qiang Yang. 2007. Document transformation for multi-label feature selection in text categorization, In Proceedings of the IEEE International Conference on Data Mining. 451--456. Google ScholarDigital Library
- Weiwei Cheng and Eyke Hüllermeier. 2009. Combining instance-based learning and logistic regression for multilabel classification. Machine Learning 76(2--3) (Sept. 2009), 211--225. Google ScholarDigital Library
- Everton Alvares Cherman, Jean Metz, and Maria Carolina Monard. 2012. Incorporating label dependency into the binary relevance framework for multi-label classification. Expert Systems Applications 39, 2 (2012), 1647--1655. Google ScholarDigital Library
- Kuo-Chen Chou, Zhi-Cheng Wu, and Xuan Xiao. 2011. iLoc-Euk: A multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins. PLoS ONE 6, 3 (2011), e18258.Google ScholarCross Ref
- Patrick Marques Ciarelli, Elias Oliveira, Claudine Badue, and Alberto Ferreira De Souza. 2009. Multi-label text categorization using a probabilistic neural network. International Journal of Computer Information Systems and Industrial Management Applications (IJCISIM) 1 (2009), 133--144.Google Scholar
- Amanda Clare. 2003. Machine Learning and Data Mining for Yeast Functional Genomics. PhD Dissertation. University of Wales, Aberystwyth.Google Scholar
- Amanda Clare and Ross D. King. 2001. Knowledge discovery in multi-label phenotype data. In Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD’01) (Lecture Notes in Computer Science), Vol. 2168. 42--53. Google ScholarDigital Library
- He Cong and Loh H. Tong. 2008. Grouping of TRIZ inventive principles to facilitate automatic patent classification. Expert Systems with Applications 34, 1 (2008), 788--795. Google ScholarDigital Library
- Koby Crammer and Yoram Singer. 2003. A family of additive online algorithms for category ranking. Journal of Machine Learning Research 3 (March 2003), 1025--1058. Google ScholarDigital Library
- André de Carvalho and Alex Freitas. 2009. A tutorial on multi-label classification techniques. In Foundations of Computational Intelligence Volume 5. Studies in Computational Intelligence, Vol. 205. Springer, Berlin, 177--195.Google Scholar
- Francesco De Comité, Rémi Gilleron, and Marc Tommasi. 2003. 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). Springer-Verlag, Berlin, 35--49. Google ScholarDigital Library
- Krzysztof Dembczyński, Weiwei Cheng, and Eyke Hüllermeier. 2010. Bayes optimal multilabel classification via probabilistic classifier chains. In Proceedings of th 27th International Conference on Machine Learning (ICML’10). 279--286.Google ScholarDigital Library
- Krzysztof Dembczyński, Willem Waegeman, Weiwei Cheng, and Eyke Hüllermeier. 2012. On label dependence and loss minimization in multi-label classification. Machine Learning 88, 1 (2012), 5--45. Google ScholarDigital Library
- A. P. Dempster, N. M. Laird, and D. B. Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistics Society -B 39(1) (1977), 1--38.Google Scholar
- Janez Demšar. 2006. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7 (2006), 1--30. Google ScholarDigital Library
- Anastasios Dimou, Grigorios Tsoumakas, Vasileios Mezaris, Ioannis Kompatsiaris, and Ioannis Vlahavas. 2009. An empirical study of multi-label learning methods for video annotation. In Proceedings of the International Workshop on Content-Based Multimedia Indexing (CBMI’09). IEEE Computer Society, Los Alamitos, CA, 19--24. Google ScholarDigital Library
- Sotiris Diplaris, Grigorios Tsoumakas, Pericles Mitkas, and Ioannis Vlahavas. 2005. Protein classification with multiple algorithms, In Proceedings of the 10th Panhellenic Conference on Informatics (PCI’05). Advances in Informatics 448--456. Google ScholarDigital Library
- R. Duwairi and A. Kassawneh. 2008. A framework for predicting proteins 3D structures. In Proceedings of the IEEE/ACS International Conference on Computer Systems and Application. Washington, DC, 37--44. Google ScholarDigital Library
- Pinar Duygulu, Kobus Barnard, Nando de Freitas, and David Forsyth. 2002. Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In Proceedings of the 7th European Conference on Computer Vision (ECCV’02) (Lecture Notes in Computer Science), Vol. 2353. 97--112. Google ScholarDigital Library
- Andre Elisseeff and Jason Weston. 2001. A kernel method for multi-labelled classification. In Advances in Neural Information Processing Systems (NIPS), Vol. 14. 681--687.Google Scholar
- Andrea Esuli and Fabrizio Sebastiani. 2009. Active learning strategies for multi-label text classification. In Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval. Lecture Notes in Computer Science, Vol. 5478. Springer-Verlag, 102--113. Google ScholarDigital Library
- Theodoros Evgeniou and Massimiliano Pontil. 2004. Regularized multi--task learning. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’04). 109--117. Google ScholarDigital Library
- Zheng Fang and Zhongfei (Mark) Zhang. 2012. Simultaneously combining multi-view multi-label learning with maximum margin classification. In ICDM. IEEE Computer Society. Google ScholarDigital Library
- Yoav Freund and Robert E. Schapire. 1997. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer System Sciences 55, 1 (1997), 119--139. Google ScholarDigital Library
- Johannes Fürnkranz, Eyke Hüllermeier, Eneldo Loza Mencía, and Klaus Brinker. 2008. Multilabel classification via calibrated label ranking. Machine Learning 73(2) (2008), 133--153. Google ScholarDigital Library
- Nadia Ghamrawi and Andrew McCallum. 2005. Collective multi-label classification. In Proceedings of the 14th ACM International Conference on Information and Knowledge Management (CIKM’05). 195--200. Google ScholarDigital Library
- Eva Gibaja and Sebastián Ventura. 2012. Multilabel Classification Library. Retrieved from http://www.citeulike.org/group/4310.Google Scholar
- Shantanu Godbole and Sunita Sarawagi. 2004. Discriminative methods for multi-labeled classification. In Proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 22--30.Google ScholarCross Ref
- Eduardo Corrêa Gonçalves, Alexandre Plastino, and Alex Alves Freitas. 2013. A genetic algorithm for optimizing the label ordering in multi-label classifier chains. In Proceedings of the IEEE 25th International Conference on Tools with Artificial Intelligence (ICTAI’13). 469--476. Google ScholarDigital Library
- Teresa Gonçalves and Paulo Quaresma. 2004. Using IR techniques to improve automated text classification, In Proceedings of the 9th International Conference on Applications of Natural Language to Information Systems. 374--379.Google ScholarCross Ref
- Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten. 2009. The WEKA data mining software: An update. SIGKDD Explorations 11(1) (2009). Google ScholarDigital Library
- Jianjun He, Hong Gu, and Zhelong Wang. 2012. Multi-instance multi-label learning based on Gaussian process with application to visual mobile robot navigation. Information Sciences 190 (2012), 162--177. Google ScholarDigital Library
- Daniel Hsu, Sham Kakade, John Langford, and Tong Zhang. 2009. Multi-label prediction via compressed sensing. In Advances in Neural Information Processing Systems 22 (NIPS), Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta (Eds.). 772--780.Google Scholar
- Xiaolei Huang, Wei Wang, Zhiyun Xue, Sameer Antani, L. Rodney Long, and Jose Jeronimo. 2008. Tissue classification using cluster features for lesion detection in digital cervigrams. In Proceedings SPIE Medical Imaging.Google ScholarCross Ref
- Eyke Hüllermeier, Johannes Fürnkranz, Weiwei Cheng, and Klaus Brinker. 2008. Label ranking by learning pairwise preferences. Artificial Intelligence 172 (2008), 1897--1916. Google ScholarDigital Library
- Marios Ioannou, George Sakkas, Grigorios Tsoumakas, and Ioannis P. Vlahavas. 2010. Obtaining bipartitions from score vectors for multi-label classification. In Proceedings of the 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI’10). 409--416. Google ScholarDigital Library
- Shuiwang Ji, Lei Tang, Shipeng Yu, and Jieping Ye. 2010. A shared-subspace learning framework for multi-label classification. ACM Transactions on Knowledge Discovery from Data 4, 2 (2010), 1--29. Google ScholarDigital Library
- Aiwen Jiang, Chunheng Wang, and Yuanping Zhu. 2008. Calibrated rank-SVM for multi-label image categorization. In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN’08). 1450--1455.Google Scholar
- Ioannis Katakis, Grigorios Tsoumakas, and Ioannis Vlahavas. 2008. Multilabel text classification for automated tag suggestion. In Proceedings of the ECML/PKDD 2008 Discovery Challenge. 1--9.Google Scholar
- Kentaro Kawai and Yoshimasa Takahashi. 2009. Identification of the dual action antihypertensive drugs using TFS-based support vector machines. Chem-Bio Informatics Journal 4 (2009), 44--51.Google Scholar
- Dragi Kocev. 2012. Ensembles for Predicting Structured Outputs. PhD Dissertation. Jǒzef Stefan International Postgraduate School.Google Scholar
- Dragi Kocev, Celine Vens, Jan Struyf, and Sašo Džeroski. 2007. Ensembles of multi-objective decision trees. In Proceedings of the 18th European Conference on Machine Learning (ECML’07). Springer-Verlag, Berlin, 624--631. Google ScholarDigital Library
- Artus Krohn-Grimberghe, Lucas Drumond, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2012. Multi-relational matrix factorization using Bayesian personalized ranking for social network data. In Proceedings of the 5th ACM International Conference on Web Search and Data Mining (WSDM’12). ACM, New York, NY, 173--182. Google ScholarDigital Library
- Neeraj Kumar, Alexander C. Berg, Peter N. Belhumeur, and Shree K. Nayar. 2009. Attribute and simile classifiers for face verification. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’09).Google Scholar
- FRI UL Bioinformatics Laboratory. 2013. Orange Multitarget Add-on for Orange Data Mining Software Package. Retrieved from http://pypi.python.org/pypi/Orange-Multitarget.Google Scholar
- J. Lafferty, A. McCallum, and F. C. N. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labelling sequence data. In Proceedings of the 18th International Conference on Machine Learning (ICML’01). 282--289. Google ScholarDigital Library
- Ken Lang. 2008. The 20 Newsgroup Dataset. Retrieved from http://people.csail.mit.edu/jrennie/20Newsgroups/.Google Scholar
- Pedro Larrañaga. 2010. Multi-label Classification. Retrieved from http://www.dynamopro.org/IMG/pdf/tamida2010-larranaga.pdf.Google Scholar
- Boris Lauser and Andreas Hotho. 2003. Automatic multi-label subject indexing in a multilingual environment. In Proceedings of the 7th European Conference, ECDL (Lecture Notes in Computer Science), Vol. 2769. 140--151.Google ScholarCross Ref
- LAWS. 2012. Proceedings of the 1st International Workshop on Learning with Weak Supervision (LAWS’12). Retrieved from http://cse.seu.edu.cn/conf/LAWS12/files/LAWS’12.pdf.Google Scholar
- David D. Lewis, Yiming Yang, Tony G. Rose, and Fan Li. 2005. RCV1: A new benchmark collection for text categorization research. Journal of Machine Learning Research 5 (2005), 361--397. Google ScholarDigital Library
- Vivian F. López, Fernando de la Prieta, Mitsunori Ogihara, and Ding Ding Wong. 2012. A model for multi-label classification and ranking of learning objects. Expert Systems with Applications 39, 10 (2012), 8878--8884. Google ScholarDigital Library
- Eneldo Loza and Johannes Fürnkranz. 2007. An evaluation of efficient multilabel classification algorithms for large-scale problems in the legal domain. In Proceedings of the LWA 2007: Lernen - Wissen - Adaption, Alexander Hinneburg (Ed.). 126--132.Google Scholar
- Eneldo Loza and Johannes Fürnkranz. 2008. 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’08). Springer-Verlag, 50--65. Google ScholarDigital Library
- Eneldo Loza and Johannes Fürnkranz. 2010. 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. 192--215. Google ScholarDigital Library
- Eneldo Loza and Johannes Fürnkranz. 2013. The EUR-Lex Dataset. Retrieved from http://www.ke.tu-darmstadt.de/resources/eurlex.Google Scholar
- Eneldo Loza, Sang-Hyeun Park, and Johannes Fürnkranz. 2009. Efficient voting prediction for pairwise multilabel classification. In Proceedings of the 17th European Symposium on Artificial Neural Networks (ESANN’09). 117--122.Google Scholar
- Aiyesha Ma, Ishwar Sethi, and Nilesh Patel. 2009. Multimedia content tagging using multilabel decision tree. In Proceedings of the 2009 11th IEEE International Symposium on Multimedia (ISM’09). 606--611. Google ScholarDigital Library
- Gjorgji Madjarov, Dejan Gjorgjevikj, and Sašo Džeroski. 2011. Dual layer voting method for efficient multi-label classification. In Proceedings of the 5th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA) (Lecture Notes in Computer Science), Vol. 6669. 232--239. Google ScholarDigital Library
- Gjorgji Madjarov, Dragi Kocev, Dejan Gjorgjevikj, and Sašo Džeroski. 2012. An extensive experimental comparison of methods for multi-label learning. Pattern Recognition 45, 9 (2012), 3084--3104. Google ScholarDigital Library
- M. A. Mammadov, A. M. Rubinov, and J. Yearwood. 2007. The study of drug-reaction relationships using global optimization techniques. Optimization Methods Software 22 (Feb. 2007), 99--126. Google ScholarDigital Library
- Andrew Kachites McCallum. 1999. Multi-label text classification with a mixture model trained by EM. In Proceedings of the AAAI 99 Workshop on Text Learning.Google Scholar
- ML2. 2012. Machine Learning. Special Issue on Learning from Multi-Label Data.Google Scholar
- MLD. 2009. Proceedings of the 1st International Workshop on Learning from Multi-Label Data (MLD’09). Retrieved from http://lpis.csd.auth.gr/workshops/mld09/mld09.pdf.Google Scholar
- MLD. 2010. Proceedings of the 2nd International Workshop on Learning from Multi-Label Data (MLD’10). Retrieved from http://cse.seu.edu.cn/conf/MLD10/files/MLD’10.pdf.Google Scholar
- MLKD. 2012. MLKD - Multilabel Library. Retrieved from http://www.citeulike.org/group/7105/tag/multilabel.Google Scholar
- Arturo Montejo-Ráez and Luis Ureña López. 2006. Selection strategies for multi-label text categorization. In Advances in Natural Language Processing. Lecture Notes in Computer Science, Vol. 4139. 585--592. Google ScholarDigital Library
- Pio Nardiello, Fabrizio Sebastiani, and Alessandro Sperduti. 2003. Discretizing continuous attributes in adaboost for text categorization. In Advances in Information Retrieval. Lecture Notes in Computer Science, Vol. 2633. 320--334. Google ScholarDigital Library
- Gulisong Nasierding and Abbas Z. Kouzani. 2010. Empirical study of multi-label classification methods for image annotation and retrieval. In Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA’10). 617--622. Google ScholarDigital Library
- Cao D. Nguyen, Tran A. Dung, and Tru H. Cao. 2005. Text classification for DAG-structured categories. In Advances in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science, Vol. 3518. 1--18. Google ScholarDigital Library
- NIPS. 2013. Extreme Classification: Multi-Class & Multi-Label Learning with Millions of Categories. Retrieved from http://nips.cc/Conferences/2013/Program/event.php??ID=3707.Google Scholar
- N. Oza, J. P. Castle, and J. Stutz. 2009. Classification of aeronautics system health and safety documents. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 39, 6 (2009), 670--680. Google ScholarDigital Library
- François Pachet and Pierre Roy. 2009. Improving multilabel analysis of music titles: A large-scale validation of the correction approach. IEEE Transactions on Audio, Speech, and Language Processing 17, 2 (2009), 335--343. Google ScholarDigital Library
- Sang-Hyeun Park and Johannes Fürnkranz. 2008. Multi-Label Classification with Label Constraints. Technical Report. Knowledge Engineering Group, TU Darmstadt.Google Scholar
- R. S. Parpinelli, H. S. Lopes, and A. A Freitas. 2002. Data mining with an ant colony optimization algorithm. IEEE Transactions on Evolutionary Computation 6, 4 (2002), 321--332. Google ScholarDigital Library
- Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay. 2011. Scikit-learn: Machine learning in python. Journal of Machine Learning Research 12 (2011), 2825--2830. Google ScholarDigital Library
- J. P. Pestian, C. Brew, P. M. Matykiewicz, D. J. Hovermale, N. Johnson, K. B. Cohen, and W. Duch. 2007. A shared task involving multi-label classification of clinical free text. In Proceedings of ACL BioNLP. 97--104. Google ScholarDigital Library
- S. Peters, L. Denoyer, and P. Gallinari. 2010. Iterative annotation of multi-relational social networks. In Proceedings of the International Conference on Advances in Social Networks Analysis and Mining (ASONAM’10). 96--103. Google ScholarDigital Library
- Guo J. Qi, Xian S. Hua, Yong Rui, Jinhui Tang, Tao Mei, and Hong J. Zhang. 2007. Correlative multi-label video annotation. In Proceedings of the 15th International Conference on Multimedia (MULTIMEDIA’07). ACM, New York, NY, 17--26. Google ScholarDigital Library
- Guo-Jun Qi, Xian-Sheng Hua, Yong Rui, Jinhui Tang, and Hong-Jiang Zhang. 2009. Two-dimensional multilabel active learning with an efficient online adaptation model for image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 10 (2009), 1880--1897. Google ScholarDigital Library
- J. Ross Quinlan. 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann. Google ScholarDigital Library
- Rafal Rak, Lukasz Kurgan, and Marek Reformat. 2008. A tree-projection-based algorithm for multi-label recurrent-item associative-classification rule generation. Data & Knowledge Engineering 64, 1 (2008), 171--197. Google ScholarDigital Library
- Jesse Read. 2008. A pruned problem transformation method for multi-label classification. In Proceedings of the NZ Computer Science Research Student Conference.Google Scholar
- Jesse Read. 2010. Scalable Multi-label Classification. PhD Dissertation. University of Waikato.Google Scholar
- Jesse Read. 2011. Advances in Multi-label Classification. Retrieved from http://users.ics.aalto.fi/jesse/talks/Charla-Malaga.pdf.Google Scholar
- Jesse Read. 2012. MEKA: A Multi-label Extension to WEKA. Retrieved from http://meka.sourceforge.net/.Google Scholar
- Jesse Read, Albert Bifet, Geoffrey Holmes, and Bernhard Pfahringer. 2010. Efficient Multi-label Classification for Evolving Data Streams. Technical Report. University of Waikato, Department of Computer Science.Google Scholar
- Jesse Read, Albert Bifet, Geoff Holmes, and Bernhard Pfahringer. 2012. Scalable and efficient multi-label classification for evolving data streams. Machine Learning 88, 1 (2012), 243--272. Google ScholarDigital Library
- Jesse Read, Bernhard Pfahringer, and Geoff Holmes. 2008. Multi-label classification using ensembles of pruned sets. In Proceedings of the 2008 8th IEEE International Conference on Data Mining (ICDM’08). IEEE Computer Society, Washington, DC, 995--1000. Google ScholarDigital Library
- Jesse Read, Bernhard Pfahringer, Geoff Holmes, and Eibe Frank. 2011. Classifier chains for multi-label classification. Machine Learning 85, 3 (2011), 1--27. Google ScholarDigital Library
- Lior Rokach, Alon Schclar, and Ehud Itach. 2014. Ensemble methods for multi-label classification. Expert Systems Applications 41, 16 (Nov. 2014), 7507--7523. Google ScholarDigital Library
- Timothy Rubin, America Chambers, Padhraic Smyth, and Mark Steyvers. 2012. Statistical topic models for multi-label document classification. Machine Learning 88, 1 (2012), 157--208. Google ScholarDigital Library
- Robert E. Schapire and Yoram Singer. 1999. Improved boosting algorithms using confidence-rated predictions. Machine Learning 37(3) (1999), 297--336. Google ScholarDigital Library
- Robert E. Schapire and Yoram Singer. 2000. BoosTexter: A boosting-based system for text categorization. Machine Learning 39, 2/3 (2000), 135--168. Google ScholarDigital Library
- Fabrizio Sebastiani. 2002. Machine learning in automated text categorization. ACM Computing Survey 34, 1 (March 2002), 1--47. Google ScholarDigital Library
- Fabrizio Sebastiani, Alessandro Sperduti, and Nicola Valdambrini. 2000. An improved boosting algorithm and its application to text categorization. In Proceedings of the 9th International Conference on Information and Knowledge Management (CIKM’00). ACM, New York, NY, 78--85. Google ScholarDigital Library
- Konstantinos Sechidis, Grigorios Tsoumakas, and Ioannis P. Vlahavas. 2011. On the stratification of multi-label data. In Proceedings of the Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD (part III) (Lecture Notes in Computer Science), Vol. 6913. 145--158. Google ScholarDigital Library
- Huan Shao, GuoZheng Li, GuoPing Liu, and YiQin Wang. 2010. Symptom selection for multi-label data of inquiry diagnosis in traditional Chinese medicine. Science China Information Sciences 1 (2010), 1--13.Google Scholar
- Jamie Shotton, John Winn, Carsten Rother, and Antonio Criminisi. 2009. TextonBoost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context. International Journal of Computer Vision 81 (2009), 2--23. Google ScholarDigital Library
- Andrew Skabar, Dennis Wollersheim, and Tim Whitfort. 2006. Multi-label classification of gene function using MLPs. In Proceedings of the International Joint Conference on Neural Networks. 2234--2240.Google Scholar
- Cees G. M. Snoek, Marcel Worring, Jan C. van Gemert, Jan-Mark Geusebroek, and Arnold W. M. Smeulders. 2006. The challenge problem for automated detection of 101 semantic concepts in multimedia. In Proceedings of ACM Multimedia. 421--430. Google ScholarDigital Library
- Tal Sobol-Shikler and Peter Robinson. 2010. Classification of complex information: Inference of co-occurring affective states from their expressions in speech. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 7 (2010), 1284--1297. Google ScholarDigital Library
- Yang Song, Lu Zhang, and C. Lee Giles. 2011. Automatic tag recommendation algorithms for social recommender systems. ACM Transactions on the Web 5, 1 (2011), 4:1--4:31. Google ScholarDigital Library
- Stephan Spat, Bruno Cadonna, Ivo Rakovac, Christian Gütl, Hubert Leitner, Günther Stark, and Peter Beck. 2008. Enhanced information retrieval from narrative German-language clinical text documents using automated document classification. In Proceedings of MIE 2008 the 21st International Congress of the European Federation for Medical Informatics. 473--478.Google Scholar
- Eleftherios Spyromitros, Grigorios Tsoumakas, and Ioannis Vlahavas. 2008. An empirical study of lazy multilabel classification algorithms. In Proceedings of the 5th Hellenic Conference on Artificial Intelligence (SETN’08). 401--406. Google ScholarDigital Library
- Ashok Srivastava and Brett Zane-Ulman. 2005. Discovering recurring anomalies in text reports regarding complex space systems. In Proceedings of the 2005 IEEE Aerospace Conference. 3853--3862.Google ScholarCross Ref
- Liang Sun, Shuiwang Ji, and Jieping Ye. 2013. Multi-Label Dimensionality Reduction. Chapman & Hall/CRC Machine Learning & Pattern Recognition.Google Scholar
- Muhammad A. Tahir, Josef Kittler, Fei Yan, and Krystian Mikolajczyk. 2009. Kernel discriminant analysis using triangular kernel for semantic scene classification. In Proceedings of the 7th International Workshop on Content-Based Multimedia Indexing (CBMI’09). IEEE, Los Alamitos, CA, 1--6. Google ScholarDigital Library
- Farbound Tai and Hsuan-Tien Lin. 2010. Multi-label classification with principal label space transformation. In Proceedings of the 2nd International Workshop on Learning from Multi-Label Data (MLD’10). 45--52.Google Scholar
- Lei Tang and Huan Liu. 2009. Relational learning via latent social dimensions. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’09). ACM, New York, NY, 817--826. Google ScholarDigital Library
- Lei Tang, Suju Rajan, and Vijay K. Narayanan. 2009. Large scale multi-label classification via metalabeler. In Proceedings of the 18th International Conference on World Wide Web. New York, NY, 211--220. Google ScholarDigital Library
- Lena Tenenboim, Lior Rokach, and Bracha Shapira. 2010. Identification of label dependencies for multi-label classification. In Proceedings of the 2nd International Workshop on Learning from Multi-Label Data (MLD’10). 53--60.Google Scholar
- Fadi A. Thabtah, Peter Cowling, and Yonghong Peng. 2004. MMAC: A new multi-class, multi-label associative classification approach. In Proceedings of the 4th IEEE International Conference on Data Mining (ICDM’04). 217--224. Google ScholarDigital Library
- Fadi A. Thabtah and Peter L. Cowling. 2007. A greedy classification algorithm based on association rule. Applied Soft Computing 7, 3 (2007), 1102--1111. Google ScholarDigital Library
- K. Trohidis, Grigorios Tsoumakas, G. Kalliris, and Ioannis Vlahavas. 2008. Multi-label classification of music into emotions. In Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR’08).Google Scholar
- Grigorios Tsoumakas, A. Dimou, E. Spyromitros, V. Mezaris, I. Kompatsiaris, and Ioannis Vlahavas. 2009. 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). 101--116.Google Scholar
- Grigorios Tsoumakas and Ioannis Katakis. 2007. Multi label classification: An overview. International Journal of Data Warehousing and Mining 3, 3 (2007), 1--13.Google ScholarCross Ref
- Grigorios Tsoumakas, Ioannis Katakis, and Ioannis Vlahavas. 2008. 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).Google Scholar
- Grigorios Tsoumakas, Ioannis Katakis, and Ioannis Vlahavas. 2010a. Data Mining and Knowledge Discovery Handbook, Part 6. Springer, Chapter Mining Multi-label Data, 667--685.Google Scholar
- Grigorios Tsoumakas, Ioannis Katakis, and Ioannis Vlahavas. 2010b. Random k-labelsets for multi-label classification. IEEE Transactions on Knowledge and Data Engineering 23, 7 (2010), 1079--1089. Google ScholarDigital Library
- Grigorios Tsoumakas, Eleftherios Spyromitros-Xioufis, Jozef Vilcek, and Ioannis Vlahavas. 2011. Mulan: A java library for multi-label learning. Journal of Machine Learning Research 12 (2011), 2411--2414. Google ScholarDigital Library
- Grigorios Tsoumakas and Ioannis Vlahavas. 2007. Random k-labelsets: An ensemble method for multilabel classification. In Proceedings of the 18th European Conference on Machine Learning (ECML’07), Vol. 4701. 406--417. Google ScholarDigital Library
- Grigorios Tsoumakas, Min Ling Zhang, and Zhi-Hua Zhou. 2009. Learning from multi-label data. ECML/PKDD’09. (September 2009).Google Scholar
- D. Turnbull, L. Barrington, D. Torres, and G. Lanckriet. 2008. Semantic annotation and retrieval of music and sound effects. IEEE Transactions on Audio, Speech, and Language Processing 16, 2 (2008), 467--476. Google ScholarDigital Library
- Naonori Ueda and Kazumi Saito. 2002a. Parametric mixture models for multi-labeled text. In Neural Information Processing Systems 15 (NIPS). 721--728.Google Scholar
- Naonori Ueda and Kazumi Saito. 2002b. Yahoo Dataset. Retrieved from http://www.kecl.ntt.co.jp/as/members/ueda/yahoo.tar.gz.Google Scholar
- Eranga Ukwatta and Jagath Samarabandu. 2009. Vision based metal spectral analysis using multi-label classification. In Proceedings of the Canadian Conference on Computer and Robot Vision (CRV’09). 132--139. Google ScholarDigital Library
- Celine Vens, Jan Struyf, Leander Schietgat, Sašo Džeroski, and Hendrik Blockeel. 2008. Decision trees for hierarchical multi-label classification. Machine Learning 73, 2 (2008), 185--214. Google ScholarDigital Library
- Sergeja Vogrincic and Zoran Bosnic. 2011. Ontology-based multi-label classification of economic articles. Computer Science and Information Systems 8, 1 (2011), 101--119.Google ScholarCross Ref
- Jingdong Wang, Yinghai Zhao, Xiuqing Wu, and Xian-Sheng Hua. 2010. A transductive multi-label learning approach for video concept detection. Pattern Recognition 44 (2010), 2274--2286. Google ScholarDigital Library
- Mei Wang, Xiangdong Zhou, and Tat S. Chua. 2008. Automatic image annotation via local multi-label classification. In Proceedings of the 2008 International Conference on Content-based Image and Video Retrieval (CIVR’08). ACM, New York, NY, 17--26. Google ScholarDigital Library
- Wei Wang and Zhi-Hua Zhou. 2008. On multi-view active learning and the combination with semi-supervised learning. In Proceedings of the 25th International Conference on Machine Learning (ICML’08). USA, 1152--1159. Google ScholarDigital Library
- David H. Wolpert. 1992. Stacked generalization. Neural Networks 5 (1992), 241--259. Google ScholarDigital Library
- Jianhua Xu. 2012. An efficient multi-label support vector machine with a zero label. Expert Systems with Applications 39, 5 (2012), 4796--4804. Google ScholarDigital Library
- Jianhua Xu. 2013a. Fast multi-label core vector machine. Pattern Recognition 46, 3 (2013), 885--898. Google ScholarDigital Library
- Jianhua Xu. 2013b. Laboratory of Intelligent Computation. Retrieved from http://computer.njnu.edu.cn/Lab/LABIC/LABIC_Software.html.Google Scholar
- Rong Yan, Jelena Tesic, and John R. Smith. 2007. Model-shared subspace boosting for multi-label classification. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 834--843. Google ScholarDigital Library
- Yan Yan, Glenn Fung, Jennifer G. Dy, and Romer Rosales. 2010. Medical coding classification by leveraging inter-code relationships. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’10). ACM, New York, NY, 193--202. Google ScholarDigital Library
- Yiming Yang. 1999. An evaluation of statistical approaches to text categorization. Information Retrieval 1, 1--2 (1999), 69--90. Google ScholarDigital Library
- Yiming Yang. 2001. 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). ACM, New York, NY, 137--145. Google ScholarDigital Library
- Yiming Yang and Siddharth Gopal. 2012. Multilabel classification with meta-level features in a learning-to-rank framework. Machine Learning 88, 1 (2012), 47--68. Google ScholarDigital Library
- Yiming Yang and Xin Liu. 1999. A re-examination of text categorization methods. In Proceedings of the 22nd Annual International SIGIR. 42--49. Google ScholarDigital Library
- Yang Yang and Bao-Liang Lu. 2006. Prediction of protein subcellular multi-locations with a min-max modular support vector machine. In Advances in Neural Networks (ISNN’06). Lecture Notes in Computer Science, Vol. 3973. Springer, Berlin, 667--673. Google ScholarDigital Library
- Yiming Yang and Jan O. Pedersen. 1997. A comparative study on feature selection in text categorization. In Proceedings of the 14th International Conference on Machine Learning (ICML’97). Morgan Kaufmann, San Francisco, CA, 412--420. Google ScholarDigital Library
- John Yearwood, Musa Mammadov, and Arunava Banerjee. 2010. Profiling phishing emails based on hyperlink information. In Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining. IEEE, 120--127. Google ScholarDigital Library
- Kai Yu, Shipeng Yu, and Volker Tresp. 2005. Multi-label informed latent semantic indexing. In SIGIR’05: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’05). ACM, New York, NY, 258--265. Google ScholarDigital Library
- Jintao Zhang and Jun Huan. 2012. Inductive multi-task learning with multiple view data. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 543--551. Google ScholarDigital Library
- Min-Ling Zhang. 2009. Ml-rbf: RBF neural networks for multi-label learning. Neural Processing Letters 29, 2 (2009), 61--74. Google ScholarDigital Library
- Min-Ling Zhang, José M. Peña, and Victor Robles. 2009. Feature selection for multi-label naive Bayes classification. Information Sciences 179, 19 (2009), 3218--3229. Google ScholarDigital Library
- Min L. Zhang and Kun Zhang. 2010. Multi-label learning by exploiting label dependency. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’10). ACM, New York, NY, 999--1008. Google ScholarDigital Library
- Min-Ling Zhang and Zhi-Hua Zhou. 2005. A k-nearest neighbor based algorithm for multi-label classification. In Proceedings of the IEEE International Conference on Granular Computing (GrC’05). 718--721.Google Scholar
- Min-Ling Zhang and Zhi-Hua Zhou. 2006. Multilabel neural networks with applications to functional genomics and text categorization. IEEE Transactions on Knowledge and Data Engineering 18, 10 (2006), 1338--1351. Google ScholarDigital Library
- Min L. Zhang and Zhi H. Zhou. 2007. Multi-label learning by instance differentiation. In AAAI. 669--674. Google ScholarDigital Library
- Min-Ling Zhang and Zhi-Hua Zhou. 2014. A review on multi-label learning algorithms. IEEE Transaction on Knowledge and Data Engineering 26, 8 (2014), 1819--1837.Google ScholarCross Ref
- Xiatian Zhang, Quan Yuan, Shiwan Zhao, Wei Fan, Wentao Zheng, and Zhong Wang. 2010. Multi-label classification without the multi-label cost. In Proceedings of the 10th SIAM International Conference on Data Mining.Google ScholarCross Ref
- Yi Zhang. 2012. Learning with Limited Supervision by Input and Output Coding. PhD Dissertation. Carnegie Mellon University. Google ScholarDigital Library
- Yi Zhang, Samuel Burer, and W. Nick Street. 2006. Ensemble pruning via semi-definite programming. Journal of Machine Learning Research 7 (2006), 1315--1338. Google ScholarDigital Library
- Yin Zhang and Zhi H. Zhou. 2010. Multilabel dimensionality reduction via dependence maximization. ACM Transactions on Knowledge Discovery from Data (TKDD) 4, 3 (2010), 14:1--14:21. Google ScholarDigital Library
- Tianyi Zhou, Dacheng Tao, and Xindong Wu. 2012a. Compressed labeling on distilled labelsets for multi-label learning. Machine Learning 88, 1--2 (2012), 69--126. Google ScholarDigital Library
- Zhi H. Zhou and Min L. Zhang. 2006. Multi-instance multi-label learning with application to scene classification. In Advances in Neural Information Processing Systems 19 (NIPS’06), Bernhard Schölkopf, John C. Platt, and Thomas Hoffman (Eds.). 1609--1616.Google Scholar
- Zhi-Hua Zhou, Min-Ling Zhang, Sheng-Jun Huang, and Yu-Feng Li. 2012b. Multi-instance multi-label learning. Artificial Intelligence 176, 1 (2012), 2291--2320. Google ScholarDigital Library
- Shenghuo Zhu, Xiang Ji, Wei Xu, and Yihong Gong. 2005. Multi-labelled classification using maximum entropy method. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’05). ACM, New York, NY, 274--281. Google ScholarDigital Library
Index Terms
- A Tutorial on Multilabel Learning
Recommendations
Transductive Multilabel Learning via Label Set Propagation
The problem of multilabel classification has attracted great interest in the last decade, where each instance can be assigned with a set of multiple class labels simultaneously. It has a wide variety of real-world applications, e.g., automatic image ...
Multilabel relationship learning
Multilabel learning problems are commonly found in many applications. A characteristic shared by many multilabel learning problems is that some labels have significant correlations between them. In this article, we propose a novel multilabel learning ...
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
In multilabel learning, each instance in the training set is associated with a set of labels and the task is to output a label set whose size is unknown a priori for each unseen instance. In this paper, this problem is addressed in the way that a neural ...
Comments