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International Work-Conference on Artificial Neural Networks

IWANN 2017: Advances in Computational Intelligence pp 73–84Cite as

A Transformation Approach Towards Big Data Multilabel Decision Trees

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10305))

Abstract

A large amount of the data processed nowadays is multilabel in nature. This means that every pattern usually belongs to several categories at once. Multilabel data are abundant, and most multilabel datasets are quite large. This causes that many multilabel classification methods struggle with their processing. Tackling this task by means of big data methods seems a logical choice. However, this approach has been scarcely explored by now. The present work introduces several big data multilabel classifiers, all of them based on decision trees. After detailing how they have been designed, their predictive performance, as well as the execution time, are analyzed.

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Notes

  1. 1.

    Card, Dens and many other multilabel characterization metrics can be easily obtained with the mldr package [14].

  2. 2.

    All of them can be found in the RUMDR [23] repository.

  3. 3.

    Names of metrics have been abbreviated to better fit them as column captions.

References

  1. Kotsiantis, S.: Supervised machine learning: a review of classification techniques. In: Proceedings of Conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real World AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies, pp. 3–24. IOS Press (2007)

    Google Scholar 

  2. Svetnik, V., Liaw, A., Tong, C., Culberson, J.C., Sheridan, R.P., Feuston, B.P.: Random forest: a classification and regression tool for compound classification and qsar modeling. J. Chem. Inf. Comput. Sci. 43(6), 1947–1958 (2003)

    Article  Google Scholar 

  3. Wieczorkowska, A., Synak, P., Raś, Z.: Multi-label classification of emotions in music. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds.) Intelligent Information Processing and Web Mining. AISC, vol. 35, pp. 307–315. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  4. Boutell, M., Luo, J., Shen, X., Brown, C.: Learning multi-label scene classification. Pattern Recogn. 37(9), 1757–1771 (2004)

    Article  Google Scholar 

  5. Charte, F., Rivera, A.J., del Jesus, M.J., Herrera, F.: QUINTA: a question tagging assistant to improve the answering ratio in electronic forums. In: Proceedings of IEEE International Conference on Computer as a Tool, EUROCON 2015, pp. 1–6. IEEE (2015)

    Google Scholar 

  6. Herrera, F., Charte, F., Rivera, A.J., Del Jesus, M.J.: Multilabel Classification: Problem Analysis, Metrics and Techniques. Springer, Heidelberg (2016)

    Book  Google Scholar 

  7. Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)

    Google Scholar 

  8. Steinberg, D., Colla, P.: CART: Tree-Structured Non-Parametric Data Analysis. Salford Systems, San Diego (1995)

    Google Scholar 

  9. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993). ISBN 1-55860-238-0

    Google Scholar 

  10. Snoek, C.G.M., Worring, M., van Gemert, J.C., Geusebroek, J.M., Smeulders, A.W.M.: The challenge problem for automated detection of 101 semantic concepts in multimedia. In: Proceedings of 14th ACM International Conference on Multimedia, MULTIMEDIA 2006, pp. 421–430 (2006)

    Google Scholar 

  11. Srivastava, A.N., Zane-Ulman, B.: Discovering recurring anomalies in text reports regarding complex space systems. In: Aerospace Conference, pp. 3853–3862. IEEE (2005)

    Google Scholar 

  12. Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. In: Advances in Neural Information Processing Systems, vol. 14, pp. 681–687. MIT Press (2001)

    Google Scholar 

  13. Herrera, F., Charte, F., Rivera, A.J., del Jesus, M.J.: Case studies and metrics. Multilabel Classification, pp. 33–63. Springer, Cham (2016). doi:10.1007/978-3-319-41111-8_3

    Google Scholar 

  14. Charte, F., Charte, D.: Working with multilabel datasets in R: the mldr package. R. J. 7(2), 149–162 (2015)

    Google Scholar 

  15. Clare, A., King, R.D.: Knowledge discovery in multi-label phenotype data. In: Raedt, L., Siebes, A. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 42–53. Springer, Heidelberg (2001). doi:10.1007/3-540-44794-6_4

    Chapter  Google Scholar 

  16. Zhang, M.: Ml-rbf: RBF neural networks for multi-label learning. Neural Process. Lett. 29, 61–74 (2009)

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  18. Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. HotCloud 10(10–10), 95 (2010)

    Google Scholar 

  19. Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The hadoop distributed file system. In: 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), pp. 1–10. IEEE (2010)

    Google Scholar 

  20. Gillick, D., Faria, A., DeNero, J.: Mapreduce: distributed computing for machine learning, Berkley, 18 December 2006

    Google Scholar 

  21. Meng, X., Bradley, J., Yavuz, B., Sparks, E., Venkataraman, S., Liu, D., Freeman, J., Tsai, D., Amde, M., Owen, S., et al.: Mllib: machine learning in apache spark. J. Mach. Learn. Res. 17(34), 1–7 (2016)

    MathSciNet  MATH  Google Scholar 

  22. del Río, S., López, V., Benítez, J.M., Herrera, F.: On the use of mapreduce for imbalanced big data using random forest. Inf. Sci. 285, 112–137 (2014)

    Article  Google Scholar 

  23. Charte, F., Charte, D., Rivera, A., de Jesus, M.J., Herrera, F.: R ultimate multilabel dataset repository. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds.) HAIS 2016. LNCS (LNAI), vol. 9648, pp. 487–499. Springer, Cham (2016). doi:10.1007/978-3-319-32034-2_41

    Chapter  Google Scholar 

  24. Crammer, K., Dredze, M., Ganchev, K., Talukdar, P.P., Carroll, S.: Automatic code assignment to medical text. In: Proceedings of Workshop on Biological, Translational, and Clinical Language Processing, BioNLP 2007, pp. 129–136. Association for Computational Linguistics (2007)

    Google Scholar 

  25. Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85, 333–359 (2011)

    Article  MathSciNet  Google Scholar 

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Acknowledgments

This work is partially supported by the Spanish Ministry of Science and Technology under project TIN2015-68454-R.

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Authors and Affiliations

  1. Department of Computer Science, University of Jaén, Jaén, Spain

    Antonio Jesús Rivera Rivas, Francisco Charte Ojeda, Francisco Javier Pulgar & Maria Jose del Jesus

Authors
  1. Antonio Jesús Rivera Rivas
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  2. Francisco Charte Ojeda
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  3. Francisco Javier Pulgar
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  4. Maria Jose del Jesus
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Corresponding author

Correspondence to Antonio Jesús Rivera Rivas .

Editor information

Editors and Affiliations

  1. Universidad de Granada , Granada, Spain

    Ignacio Rojas

  2. University of Malaga , Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Vilanova i la Geltrú, Barcelona, Spain

    Andreu Catala

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Rivera Rivas, A.J., Charte Ojeda, F., Pulgar, F.J., del Jesus, M.J. (2017). A Transformation Approach Towards Big Data Multilabel Decision Trees. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10305. Springer, Cham. https://doi.org/10.1007/978-3-319-59153-7_7

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  • DOI: https://doi.org/10.1007/978-3-319-59153-7_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59152-0

  • Online ISBN: 978-3-319-59153-7

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