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An Empirical Comparison of Methods for Multi-label Data Stream Classification

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Advances in Big Data (INNS 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 529))

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Abstract

This paper studies the problem of multi-label classification in the context of data streams. We discuss related work in this area and present our implementation of several existing approaches as part of the Mulan software. We present empirical results on a real-world data stream concerning media monitoring and discuss and draw a number of conclusions regarding their performance.

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Notes

  1. 1.

    http://storm.apache.org/.

  2. 2.

    http://samza.apache.org/.

  3. 3.

    http://www.datascouting.com/.

References

  1. Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: On demand classification of data streams. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2004, pp. 503–508. ACM, New York (2004). http://doi.acm.org/10.1145/1014052.1014110

  2. Bifet, A., Holmes, G., Pfahringer, B., Kranen, P., Kremer, H., Jansen, T., Seidl, T.: Moa: massive online analysis, a framework for stream classification and clustering. In: Invited Presentation at the International Workshop on Handling Concept Drift in Adaptive Information Systems in Conjunction with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2010), pp. 3–16 (2010)

    Google Scholar 

  3. 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 2000, pp. 71–80. ACM, New York (2000). http://doi.acm.org/10.1145/347090.347107

  4. Kong, X., Yu, P.S.: An ensemble-based approach to fast classification of multi-label data streams. In: Georgakopoulos, D., Joshi, J.B.D. (eds.) 7th International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2011, Orlando, FL, USA, pp. 95–104. ICST/IEEE, 15-18 October 2011. http://dx.doi.org/10.4108/icst.collaboratecom.2011.247086

  5. Morales, G.D.F., Bifet, A.: Samoa: scalable advanced massive online analysis. J. Mach. Learn. Res. 16, 149–153 (2015). http://jmlr.org/papers/v16/morales15a.html

    Google Scholar 

  6. Neumeyer, L., Robbins, B., Nair, A., Kesari, A.: S4: distributed stream computing platform. In: Proceedings of the 2010 IEEE International Conference on Data Mining Workshops, ICDMW 2010, pp. 170–177. IEEE Computer Society, Washington, DC (2010). http://dx.doi.org/10.1109/ICDMW.2010.172

  7. Read, J., Bifet, A., Holmes, G., Pfahringer, B.: Scalable and efficient multi-label classification for evolving data streams. Mach. Learn. 88(1–2), 243–272 (2012). http://dx.doi.org/10.1007/s10994-012-5279-6

    Article  MathSciNet  Google Scholar 

  8. Read, J., Reutemann, P., Pfahringer, B., Holmes, G.: MEKA: a multi-label/multi-target extension to Weka. J. Mach. Learn. Res. 17(21), 1–5 (2016). http://jmlr.org/papers/v17/12-164.html

    MathSciNet  MATH  Google Scholar 

  9. Shi, Z., Xue, Y., Wen, Y., Cai, G.: Efficient class incremental learning for multi-label classification of evolving data streams. In: 2014 International Joint Conference on Neural Networks, IJCNN 2014, Beijing, China, pp. 2093–2099. IEEE, 6-11 July 2014. http://dx.doi.org/10.1109/IJCNN.2014.6889926

  10. Tsoumakas, G., Vlahavas, I.: Random k-labelsets: an ensemble method for multilabel classification. In: Kok, J.N., Koronacki, J., Mantaras, R.L., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 406–417. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74958-5_38

    Chapter  Google Scholar 

  11. Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, 2nd edn, pp. 667–685. Springer, Heidelberg (2010). Chap. 34

    Google Scholar 

  12. Tsoumakas, G., et al.: WISE 2014 challenge: multi-label classification of print media articles to topics. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds.) Web Information Systems Engineering - WISE 2014. LNCS, vol. 8787, pp. 541–548. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  14. Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2003, pp. 226–235. ACM, New York (2003). http://doi.acm.org/10.1145/956750.956778

  15. Wang, P., Zhang, P., Guo, L.: Mining multi-label data streams using ensemble-based active learning. In: SDM, pp. 1131–1140 (2012)

    Google Scholar 

  16. Xioufis, E.S., Spiliopoulou, M., Tsoumakas, G., Vlahavas, I.P.: Dealing with concept drift and class imbalance in multi-label stream classification. In: Walsh, T. (ed.) IJCAI, pp. 1583–1588. IJCAI/AAAI (2011)

    Google Scholar 

  17. Zhang, M.L., Zhou, Z.H.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038–2048 (2007)

    Article  MATH  Google Scholar 

  18. Zliobaite, I., Bifet, A., Read, J., Pfahringer, B., Holmes, G.: Evaluation methods and decision theory for classification of streaming data with temporal dependence. Mach. Learn. 98(3), 455–482 (2015). http://dx.doi.org/10.1007/s10994-014-5441-4

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

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

    Konstantina Karponi & Grigorios Tsoumakas

Authors
  1. Konstantina Karponi
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  2. Grigorios Tsoumakas
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Corresponding author

Correspondence to Konstantina Karponi .

Editor information

Editors and Affiliations

  1. School of Computing and Communications, Lancaster University , Lancaster, United Kingdom

    Plamen Angelov

  2. Data Engineering Lab, Dept. of Informatics, Aristotle University of Thessaloniki , Thessaloniki, Greece

    Yannis Manolopoulos

  3. Lab of Forest Informatics (FiLAB), Democritus University of Thrace , Orestiada, Greece

    Lazaros Iliadis

  4. WPC Information Systems Faculty, Arizona State University , Tempe, Arizona, USA

    Asim Roy

  5. Electrical Engineering Dept, (ICA), Pontifical Catholic Univ of Rio de Janei , Rio de Janeiro, Rio de Janeiro, Brazil

    Marley Vellasco

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Karponi, K., Tsoumakas, G. (2017). An Empirical Comparison of Methods for Multi-label Data Stream Classification. In: Angelov, P., Manolopoulos, Y., Iliadis, L., Roy, A., Vellasco, M. (eds) Advances in Big Data. INNS 2016. Advances in Intelligent Systems and Computing, vol 529. Springer, Cham. https://doi.org/10.1007/978-3-319-47898-2_16

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  • DOI: https://doi.org/10.1007/978-3-319-47898-2_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47897-5

  • Online ISBN: 978-3-319-47898-2

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