Top

Hint

Swipe to navigate through the chapters of this book

Published in:

Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Interdependence Model for Multi-label Classification

Authors : Kosuke Yoshimura, Tomoaki Iwase, Yukino Baba, Hisashi Kashima

Published in: Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series

Publisher: Springer International Publishing

Abstract

The multi-label classification problem is a supervised learning problem that aims to predict multiple labels for each data instance. One of the key issues in designing multi-label learning approaches is how to incorporate dependencies among different labels. In this study, we propose a new approach called the interdependence model, which consists of a set of single-label predictors each of which predicts a particular label using the other labels. The proposed model can directly consider label interdependencies by reusing arbitrary conventional probabilistic models for single-label classification. We consider three prediction methods and one accelerated method for making predictions with the interdependence model. Experiments show the superior prediction performance of the proposed methods in several evaluation metrics, especially when there is a large number of candidate labels or when labels are partially given in the test phase.

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt 90 Tage mit der neuen Mini-Lizenz testen!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt 90 Tage mit der neuen Mini-Lizenz testen!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt 90 Tage mit der neuen Mini-Lizenz testen!

inform now

previous chapter An Improved Method of Applying a Machine Translation Model to a Chinese Word Segmentation Task

next chapter Combining Deep Learning and (Structural) Feature-Based Classification Methods for Copyright-Protected PDF Documents

http://mulan.sourceforge.net.

Balasubramanian, K., Lebanon, G.: The landmark selection method for multiple output prediction. In: Proceedings of the 29th International Conference on Machine Learning, pp. 283–290 (2012)

Bhatia, K., Jain, H., Kar, P., Varma, M., Jain, P.: Sparse local embeddings for extreme multi-label classification. In: Advances in Neural Information Processing Systems, vol. 28, pp. 730–738 (2015)

Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recognit. 37(9), 1757–1771 (2004). https://doi.org/10.1016/j.patcog.2004.03.009 CrossRef

Briggs, F., et al.: The 9th Annual MLSP Competition: New Methods for Acoustic Classification of Multiple Simultaneous Bird Species in a Noisy Environment (2013). https://doi.org/10.1109/MLSP.2013.6661934

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

Godbole, S., Sarawagi, S.: Discriminative methods for multi-labeled classification. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 22–30. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24775-3_5 CrossRef

Goncalves, E.C., Plastino, A., Freitas, A.A.: A genetic algorithm for optimizing the label ordering in multi-label classifier chains. In: Proceedings of the 25th International Conference on Tools with Artificial Intelligence, pp. 469–476 (2013). https://doi.org/10.1109/ICTAI.2013.76

Hsu, D., Kakade, S.M., Langford, J., Zhang, T.: Multi-label prediction via compressed sensing. In: Advances in Neural Information Processing Systems, vol. 22, pp. 772–780 (2009)

Klimt, B., Yang, Y.: The Enron corpus: a new dataset for email classification research. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 217–226. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30115-8_22 CrossRef

10.

Pestian, J.P., et al.: A shared task involving multi-label classification of clinical free text. In: Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing, pp. 97–104 (2007)

11.

Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS (LNAI), vol. 5782, pp. 254–269. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04174-7_17 CrossRef

12.

Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37(3), 297–336 (1999). https://doi.org/10.1023/A:1007614523901 CrossRefMATH

13.

Tagami, Y.: AnnexML: approximate nearest neighbor search for extreme multi-label classification. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 455–464 (2017). https://doi.org/10.1145/3097983.3097987

14.

Tagami, Y.: Learning extreme multi-label tree-classifier via nearest neighbor graph partitioning. In: Proceedings of the 26th International Conference on World Wide Web, pp. 845–846 (2017). https://doi.org/10.1145/3041021.3054204

15.

Tai, F., Lin, H.T.: Multilabel classification with principal label space transformation. Neural Comput. 24(9), 2508–2542 (2012). https://doi.org/10.1162/NECO_a_00320 MathSciNetCrossRefMATH

16.

Trohidis, K., Tsoumakas, G., Kalliris, G., Vlahavas, I.P.: Multi-label classification of music into emotions. In: Proceedings of the 9th International Conference on Music Information Retrieval, vol. 8, pp. 325–330 (2008). https://doi.org/10.1186/1687-4722-2011-426793

17.

Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehous. Min. 2007, 1–13 (2007). https://doi.org/10.4018/jdwm.2007070101 CrossRef

18.

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

19.

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). https://doi.org/10.1007/978-3-540-74958-5_38 CrossRef

20.

Zhou, W.-J., Yu, Y., Zhang, M.L.: Binary linear compression for multi-label classification. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 3546–3552 (2017). https://doi.org/10.24963/ijcai.2017/496

21.

Zhang, Y., Schneider, J.: Multi-label output codes using canonical correlation analysis. In: Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, vol. 15, pp. 873–882 (2011)

Title: Interdependence Model for Multi-label Classification
Authors: Kosuke Yoshimura
Tomoaki Iwase
Yukino Baba
Hisashi Kashima
Publisher: Springer International Publishing
Book: Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series
Print ISBN: 978-3-030-30489-8

Electronic ISBN: 978-3-030-30490-4

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-30490-4_6

Springer Professional

Hint

Swipe to navigate through the chapters of this book

Abstract

Please log in to get access to this content

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner