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Exploiting Label Interdependencies in Multi-label Classification

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Progress in Computer Recognition Systems (CORES 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 977))

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Abstract

Multi-label classification problems very often concern multidimensional datasets, thus, performance of the method is problematic in many cases. Exploiting label dependencies may ameliorate classification results. In the paper, new effective problem transformation method which uses label interdependencies is introduced. Experiments conducted on several benchmarking datasets showed the good performance of the presented technique, regarding six evaluation metrics, including the most restricting Classification Accuracy and confirmed by statistical inference. The obtained results are compared with those obtained by the most popular problem transformation methods.

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References

  1. MULAN: A Java Library for Multi-Label Learning. http://mulan.sourceforge.net/

  2. Weka 3: Data mining software in Java. http://www.cs.waikato.ac.nz/ml/weka/index.html

  3. Choi SS, Cha SH, Tappert CC (2010) A survey of binary similarity and distance measures. J Syst Cybern Inform 8(1):43–48

    Google Scholar 

  4. Dembczyński K, Cheng W, Hüllermeier E (2010) Bayes optimal multilabel classification via probabilistic classifier chains. In: Fürnkranz J, Joachims, T (eds) Proceedings of the 27th international conference on machine learning (ICML 2010). Omnipress, pp 279–286

    Google Scholar 

  5. Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MathSciNet  MATH  Google Scholar 

  6. Fürnkranz J, Hüllermeier E, Mencía EL, Brinker K (2008) Multilabel classification via calibrated label ranking. Mach Learn 73(2):133–153. https://doi.org/10.1007/s10994-008-5064-8

    Article  Google Scholar 

  7. Glinka K, Zakrzewska D (2016) Effective multi-label classification method for multidimensional datasets. In: Andreasen T et al (eds) Flexible query answering systems 2015: Proceedings of the 11th international conference FQAS 2015, Cracow, Poland, October 2015. Springer International Publishing, pp 127–138. https://doi.org/10.1007/978-3-319-26154-6_10

    Google Scholar 

  8. Herrera F, Charte F, Rivera AJ, del Jesus MJ (2016) Multilabel classification: problem analysis, metrics and techniques, 1st edn. Springer Publishing Company Incorporated, Heidelberg

    Google Scholar 

  9. Huang J, Li G, Wang S, Zhang W, Huang Q (2015) Group sensitive classifier chains for multi-label classification. In: 2015 IEEE international conference on multimedia and expo (ICME), pp 1–6. https://doi.org/10.1109/ICME.2015.7177400

  10. Madjarov G, Kocev D, Gjorgjevikj D, Džeroski S (2012) An extensive experimental comparison of methods for multi-label learning. Pattern Recogn 45(9):3084–3104. https://doi.org/10.1016/j.patcog.2012.03.004

    Article  Google Scholar 

  11. McHugh ML (2013) The chi-square test of independence. Biochemia Medica 23(2):143–149. https://doi.org/10.11613/BM.2013.018

    Article  Google Scholar 

  12. Read J (2008) A pruned problem transformation method for multi-label classification. In: Proceedings of the 2008 New Zealand computer science research student conference, NZCSRS, pp 143–150

    Google Scholar 

  13. Read J, Pfahringer B, Holmes G (2008) Multi-label classification using ensembles of pruned sets. In: 2008 eighth IEEE international conference on data mining, pp 995–1000. https://doi.org/10.1109/ICDM.2008.74

  14. Read J, Pfahringer B, Holmes G, Frank E (2009) Classifier chains for multi-label classification. In: Buntine W, Grobelnik M, Mladenic D, Shawe-Taylor J (eds) Machine learning and knowledge discovery in databases. LNCS, vol 5782. Springer-Verlag, pp 254–269. https://doi.org/10.1007/978-3-642-04174-7_17

    Chapter  Google Scholar 

  15. Read J, Pfahringer B, Holmes G, Frank E (2011) Classifier chains for multi-label classification. Mach Learn 85(3):333–359. https://doi.org/10.1007/s10994-011-5256-5

    Article  MathSciNet  Google Scholar 

  16. Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45:427–437

    Article  Google Scholar 

  17. Tsoumakas G, Katakis I, Vlahavas I (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 2008)

    Google Scholar 

  18. Tsoumakas G, Katakis I, Vlahavas I (2010) Mining multi-label data. In: Maimon O, Rokach, L (eds) Data mining and knowledge discovery handbook. Springer, pp 667–685. https://doi.org/10.1007/978-0-387-09823-4_34

    Chapter  Google Scholar 

  19. Wosiak A, Glinka K, Zakrzewska D (2018) Multi-label classification methods for improving comorbidities identification. Comput Biol Med 100:279–288. https://doi.org/10.1016/j.compbiomed.2017.07.006

    Article  Google Scholar 

  20. Ye C, Wu J, Sheng VS, Zhao P, Cui Z (2015) Multi-label active learning with label correlation for image classification. In: 2015 IEEE international conference on image processing (ICIP), pp 3437–3441. https://doi.org/10.1109/ICIP.2015.7351442

  21. Zhang M, Zhou Z (2014) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837. https://doi.org/10.1109/TKDE.2013.39

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Information Technology, Lodz University of Technology, Wólczańska 215, Lodz, Poland

    Kinga Glinka, Agnieszka Wosiak & Danuta Zakrzewska

Authors
  1. Kinga Glinka
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  2. Agnieszka Wosiak
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  3. Danuta Zakrzewska
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Corresponding author

Correspondence to Kinga Glinka .

Editor information

Editors and Affiliations

  1. Faculty of Electronics, Wroclaw University of Science and Technology, Wrocław, Poland

    Robert Burduk

  2. Faculty of Electronics, Wroclaw University of Science and Technology, Wrocław, Poland

    Marek Kurzynski

  3. Faculty of Electronics, Wroclaw University of Science and Technology, Wrocław, Poland

    Michał Wozniak

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Glinka, K., Wosiak, A., Zakrzewska, D. (2020). Exploiting Label Interdependencies in Multi-label Classification. In: Burduk, R., Kurzynski, M., Wozniak, M. (eds) Progress in Computer Recognition Systems. CORES 2019. Advances in Intelligent Systems and Computing, vol 977. Springer, Cham. https://doi.org/10.1007/978-3-030-19738-4_7

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