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Data Mining and Knowledge Discovery Handbook pp 667–685Cite as

Mining Multi-label Data

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Abstract

A large body of research in supervised learning deals with the analysis of single-label data, where training examples are associated with a single label λ from a set of disjoint labels L. However, training examples in several application domains are often associated with a set of labels Y ⊆ L. Such data are called multi-label.

Textual data, such as documents and web pages, are frequently annotated with more than a single label. For example, a news article concerning the reactions of the Christian church to the release of the “Da Vinci Code” film can be labeled as both religion and movies. The categorization of textual data is perhaps the dominant multi-label application.

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References

  • Barutcuoglu, Z., Schapire, R. E. & Troyanskaya, O. G. (2006). Bioinformatics 22, 830–836.

    Article  Google Scholar 

  • Blockeel, H., Schietgat, L., Struyf, J., Dz?eroski, S. & Clare, A. (2006). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4213 LNAI, 18–29.

    Google Scholar 

  • Boleda, G., im Walde, S. S. & Badia, T. (2007). In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning pp. 171–180,, Prague.

    Google Scholar 

  • Boutell, M., Luo, J., Shen, X. & Brown, C. (2004). Pattern Recognition 37, 1757–1771.

    Article  Google Scholar 

  • Brinker, K., Fürnkranz, J. & Hüllermeier, E. (2006). In Proceedings of the 17th European Conference on Artificial Intelligence (ECAI ’06) pp. 489–493„ Riva del Garda, Italy.

    Google Scholar 

  • Brinker, K. & Hüllermeier, E. (2007). In Proceedings of the 20th International Conference on Artificial Intelligence (IJCAI ’07) pp. 702–707„ Hyderabad, India.

    Google Scholar 

  • Caruana, R. (1997). Machine Learning 28, 41–75.

    Article  Google Scholar 

  • Cesa-Bianchi, N., Gentile, C. & Zaniboni, L. (2006a). In ICML ’06: Proceedings of the 23rd international conference on Machine learning pp. 177–184,.

    Google Scholar 

  • Cesa-Bianchi, N., Gentile, C.& Zaniboni, L. (2006b). Journal of Machine Learning Research 7, 31–54.

    MathSciNet  Google Scholar 

  • Chang, C.-C. & Lin, C.-J. (2001). LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

  • Chawla, N. V., Japkowicz, N. & Kotcz, A. (2004). SIGKDD Explorations 6, 1–6.

    Article  Google Scholar 

  • Chen, W., Yan, J., Zhang, B., Chen, Z. & Yang, Q. (2007). In Proc. 7th IEEE International Conference on Data Mining pp. 451–456, IEEE Computer Society, Los Alamitos, CA, USA.

    Google Scholar 

  • Clare, A. & King, R. (2001). In Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD 2001) pp. 42–53„ Freiburg, Germany.

    Google Scholar 

  • Crammer, K. & Singer, Y. (2003). Journal of Machine Learning Research 3, 1025–1058.

    Article  MATH  MathSciNet  Google Scholar 

  • de Comite, F., Gilleron, R. & Tommasi, M. (2003). In Proceedings of the 3rd International Conference on Machine Learning and Data Mining in Pattern Recognition (MLDM 2003) pp. 35–49„ Leipzig, Germany.

    Google Scholar 

  • Diplaris, S., Tsoumakas, G., Mitkas, P. & Vlahavas, I. (2005). In Proceedings of the 10th Panhellenic Conference on Informatics (PCI 2005) pp. 448–456„ Volos, Greece.

    Google Scholar 

  • Elisseeff, A. & Weston, J. (2002). In Advances in Neural Information Processing Systems 14.

    Google Scholar 

  • Esuli, A., Fagni, T. & Sebastiani, F. (2008). Information Retrieval 11, 287–313.

    Article  Google Scholar 

  • Fürnkranz, J., Hüllermeier, E., Mencia, E. L. & Brinker, K. (2008). Machine Learning .

    Google Scholar 

  • Gao, S., Wu, W., Lee, C.-H. & Chua, T.-S. (2004). In Proceedings of the 21st international conference on Machine learning (ICML ’04) p. 42„ Banff, Alberta, Canada.

    Google Scholar 

  • Ghamrawi, N. & McCallum, A. (2005). In Proceedings of the 2005 ACM Conference on Information and Knowledge Management (CIKM ’05) pp. 195–200„ Bremen, Germany.

    Google Scholar 

  • Godbole, S. & Sarawagi, S. (2004). In Proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2004) pp. 22–30,.

    Google Scholar 

  • Harris, M. A., Clark, J., Ireland, A., Lomax, J., Ashburner, M., Foulger, R., Eilbeck, K., Lewis, S., Marshall, B., Mungall, C., Richter, J., Rubin, G. M., Blake, J. A., Bult, C., Dolan, M., Drabkin, H., Eppig, J. T., Hill, D. P., Ni, L., Ringwald, M., Balakrishnan, R., Cherry, J. M., Christie, K. R., Costanzo, M. C., Dwight, S. S., Engel, S., Fisk, D. G., Hirschman, J. E., Hong, E. L., Nash, R. S., Sethuraman, A., Theesfeld, C. L., Botstein, D., Dolinski, K., Feierbach, B., Berardini, T., Mundodi, S., Rhee, S. Y., Apweiler, R., Barrell, D., Camon, E., Dimmer, E., Lee, V., Chisholm, R., Gaudet, P., Kibbe, W., Kishore, R., Schwarz, E. M., Sternberg, P., Gwinn, M., Hannick, L., Wortman, J., Berriman, M., Wood, V., de La, Tonellato, P., Jaiswal, P., Seigfried, T. & White, R. (2004). Nucleic Acids Res 32.

    Google Scholar 

  • Hüllermeier, E., Fürnkranz, J., Cheng, W. & Brinker, K. (2008). Artificial Intelligence 172, 1897–1916.

    Article  MATH  MathSciNet  Google Scholar 

  • Ji, S., Tang, L., Yu, S. & Ye, J. (2008). In Proceedings of the 14th SIGKDD International Conferece on Knowledge Discovery and Data Mining, Las Vegas, USA.

    Google Scholar 

  • Jin, R. & Ghahramani, Z. (2002). In Proceedings of Neural Information Processing Systems 2002 (NIPS 2002), Vancouver, Canada.

    Google Scholar 

  • Katakis, I., Tsoumakas, G. & Vlahavas, I. (2008). In Proceedings of the ECML/PKDD 2008 Discovery Challenge, Antwerp, Belgium.

    Google Scholar 

  • Kohavi, R. & John, G. H. (1997). Artificial Intelligence 97, 273–324.

    Article  MATH  Google Scholar 

  • Lewis, D. D., Yang, Y., Rose, T. G. & Li, F. (2004). J. Mach. Learn. Res. 5, 361–397.

    Google Scholar 

  • Li, T. & Ogihara, M. (2003). In Proceedings of the International Symposium on Music Information Retrieval pp. 239–240„ Washington D.C., USA.

    Google Scholar 

  • Li, T. & Ogihara, M. (2006). IEEE Transactions on Multimedia 8, 564–574.

    Article  Google Scholar 

  • Loza Mencia, E. & Fürnkranz, J. (2008a). In 2008 IEEE International Joint Conference on Neural Networks (IJCNN-08) pp. 2900–2907„ Hong Kong.

    Google Scholar 

  • Loza Mencia, E. & Fürnkranz, J. (2008b). In 12th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2008 pp. 50–65„ Antwerp, Belgium.

    Google Scholar 

  • Luo, X. & Zincir-Heywood, A. (2005). In Proceedings of the 15th International Symposium on Methodologies for Intelligent Systems pp. 161–169,.

    Google Scholar 

  • Maron, O. & p Erez, T. A. L. (1998). In Advances in Neural Information Processing Systems 10 pp. 570–576, MIT Press.

    Google Scholar 

  • McCallum, A. (1999). In Proceedings of the AAAI’ 99 Workshop on Text Learning.

    Google Scholar 

  • Mencia, E. L. & Fürnkranz, J. (2008). In 12th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2008, Antwerp, Belgium.

    Google Scholar 

  • Moskovitch, R., Cohenkashi, S., Dror, U., Levy, I., Maimon, A. & Shahar, Y. (2006). Artificial Intelligence in Medicine 37, 177–190.

    Article  Google Scholar 

  • Park, C. H. & Lee, M. (2008). Pattern Recogn. Lett. 29, 878–887.

    Article  Google Scholar 

  • Pestian, J. P., Brew, C., Matykiewicz, P., Hovermale, D. J., Johnson, N., Cohen, K. B. & Duch, W. (2007). In BioNLP ’07: Proceedings of the Workshop on BioNLP 2007 pp. 97–104, Association for Computational Linguistics, Morristown, NJ, USA.

    Google Scholar 

  • Qi, G.-J., Hua, X.-S., Rui, Y., Tang, J., Mei, T. & Zhang, H.-J. (2007). In MULTIMEDIA ’07: Proceedings of the 15th international conference on Multimedia pp. 17–26, ACM, New York, NY, USA.

    Google Scholar 

  • Read, J. (2008). In Proc. 2008 New Zealand Computer Science Research Student Conference (NZCSRS 2008) pp. 143–150,.

    Google Scholar 

  • Rokach L., Genetic algorithm-based feature set partitioning for classification problems, Pattern Recognition, 41(5):1676–1700, 2008.

    Article  MATH  Google Scholar 

  • Rokach L., Mining manufacturing data using genetic algorithm-based feature set decomposition, Int. J. Intelligent Systems Technologies and Applications, 4(1):57-78, 2008.

    Article  Google Scholar 

  • Rokach L., Maimon O. and Lavi I., Space Decomposition In Data Mining: A Clustering Approach, Proceedings of the 14th International Symposium On Methodologies For Intelligent Systems, Maebashi, Japan, Lecture Notes in Computer Science, Springer-Verlag, 2003, pp. 24–31.

    Google Scholar 

  • Rousu, J., Saunders, C., Szedmak, S. & Shawe-Taylor, J. (2006). Journal of Machine Learning Research 7, 1601–1626.

    MathSciNet  Google Scholar 

  • Ruepp, A., Zollner, A., Maier, D., Albermann, K., Hani, J., Mokrejs, M., Tetko, I., Güldener, U., Mannhaupt, G., Münsterkötter, M. & Mewes, H. W. (2004). Nucleic Acids Res 32, 5539–5545.

    Article  Google Scholar 

  • Schapire, R.E. Singer, Y. (2000). Machine Learning 39, 135–168.

    Article  MATH  Google Scholar 

  • Snoek, C. G. M.,Worring, M., van Gemert, J. C., Geusebroek, J.-M. & Smeulders, A.W. M. (2006). In MULTIMEDIA ’06: Proceedings of the 14th annual ACM international conference on Multimedia pp. 421–430, ACM, New York, NY, USA.

    Google Scholar 

  • Spyromitros, E., Tsoumakas, G. & Vlahavas, I. (2008). In Proc. 5th Hellenic Conference on Artificial Intelligence (SETN 2008).

    Google Scholar 

  • Srivastava, A. & Zane-Ulman, B. (2005). In IEEE Aerospace Conference.

    Google Scholar 

  • Streich, A. P. & Buhmann, J. M. (2008). In 12th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2008, Antwerp, Belgium.

    Google Scholar 

  • Sun, A. & Lim, E.-P. (2001). In ICDM ’01: Proceedings of the 2001 IEEE International Conference on Data Mining pp. 521–528, IEEE Computer Society, Washington, DC, USA.

    Google Scholar 

  • Sun, L., Ji, S. & Ye, J. (2008). In Proceedings of the 14th SIGKDD International Conferece on Knowledge Discovery and Data Mining, Las Vegas, USA.

    Google Scholar 

  • Thabtah, F., Cowling, P. & Peng, Y. (2004). In Proceedings of the 4th IEEE International Conference on Data Mining, ICDM ’04 pp. 217–224,.

    Google Scholar 

  • Trohidis, K., Tsoumakas, G., Kalliris, G. & Vlahavas, I. (2008). In Proc. 9th International Conference on Music Information Retrieval (ISMIR 2008), Philadelphia, PA, USA, 2008.

    Google Scholar 

  • Tsoumakas, G. & Katakis, I. (2007). International Journal of Data Warehousing and Mining 3, 1–13.

    Google Scholar 

  • Tsoumakas, G., Katakis, I. & Vlahavas, I. (2008). In Proc. ECML/PKDD 2008 Workshop on Mining Multidimensional Data (MMD’08) pp. 30–44,.

    Google Scholar 

  • Tsoumakas, G. & Vlahavas, I. (2007). In Proceedings of the 18th European Conference on Machine Learning (ECML 2007) pp. 406–417„ Warsaw, Poland.

    Google Scholar 

  • Ueda, N. & Saito, K. (2003). Advances in Neural Information Processing Systems 15 , 721–728.

    Google Scholar 

  • Veloso, A., Wagner, M. J., Goncalves, M. & Zaki, M. (2007). In Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2007) vol. LNAI 4702, pp. 605–612, Springer, Warsaw, Poland.

    Google Scholar 

  • Vembu, S. & Gärtner, T. (2009). In Preference Learning, (Fürnkranz, J. & Hüllermeier, E., eds),. Springer.

    Google Scholar 

  • Vens, C., Struyf, J., Schietgat, L., Džeroski, S. & Blockeel, H. (2008). Machine Learning 73, 185–214.

    Article  Google Scholar 

  • Wieczorkowska, A., Synak, P. & Ras, Z. (2006). In Proceedings of the 2006 International Conference on Intelligent Information Processing and Web Mining (IIPWM’06) pp. 307–315,.

    Google Scholar 

  • Wolpert, D. (1992). Neural Networks 5, 241–259.

    Article  Google Scholar 

  • Yang, S., Kim, S.-K. & Ro, Y. M. (2007). Circuits and Systems for Video Technology, IEEE Transactions on 17, 324–335.

    Google Scholar 

  • Yang, Y. (1999). Journal of Information Retrieval 1, 67–88.

    Google Scholar 

  • Yang, Y. & Pedersen, J. O. (1997). In Proceedings of ICML-97, 14th International Conference on Machine Learning, (Fisher, D. H., ed.), pp. 412–420, Morgan Kaufmann Publishers, San Francisco, US, Nashville, US.

    Google Scholar 

  • Yu, K., Yu, S. & Tresp, V. (2005). In SIGIR ’05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval pp. 258– 265, ACM Press, Salvador, Brazil.

    Google Scholar 

  • Zha, Z.-J., Hua, X.-S., Mei, T., Wang, J., Qi, G.-J. & Wang, Z. (2008). In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on pp. 1–8,.

    Google Scholar 

  • Zhang, M.-L.&Zhou, Z.-H. (2006). IEEE Transactions on Knowledge and Data Engineering 18, 1338–1351.

    Article  Google Scholar 

  • Zhang, M.-L. & Zhou, Z.-H. (2007a). Pattern Recognition 40, 2038–2048.

    Article  MATH  Google Scholar 

  • Zhang, M.-L. & Zhou, Z.-H. (2007b). In Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence pp. 669–674, AAAI Press, Vancouver, Britiths Columbia, Canada.

    Google Scholar 

  • Zhang, Y., Burer, S. & Street, W. N. (2006). Journal of Machine Learning Research 7, 1315–1338.

    MathSciNet  Google Scholar 

  • Zhang, Y. & Zhou, Z.-H. (2008). In Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, AAAI 2008 pp. 1503–1505, AAAI Press, Chicago, Illinois, USA.

    Google Scholar 

  • Zhou, Z.-H. (2007). In Proceedings of the 3rd International Conference on Advanced Data Mining and Applications (ADMA’07) p. 1. Springer.

    Google Scholar 

  • Zhou, Z. H. & Zhang, M. L. (2006). In NIPS, (Schölkopf, B., Platt, J. C. & Hoffman, T., eds), pp. 1609–1616, MIT Press.

    Google Scholar 

  • Zhu, S., Ji, X., Xu, W. & Gong, Y. (2005). In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in Information Retrieval pp. 274– 281.

    Google Scholar 

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  1. Dept. of Informatics, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece

    Grigorios Tsoumakas, Ioannis Katakis & Ioannis Vlahavas

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  1. Grigorios Tsoumakas
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Correspondence to Grigorios Tsoumakas .

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  1. , Dept. Industrial Engineering, Tel Aviv University, Ramat Aviv, 69978, Israel

    Oded Maimon

  2. , Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel

    Lior Rokach

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Tsoumakas, G., Katakis, I., Vlahavas, I. (2009). Mining Multi-label Data. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09823-4_34

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