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International Conference on Artificial Neural Networks

ICANN 2019: Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series pp 55–68Cite as

Interdependence Model for Multi-label Classification

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 11730)

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.

Keywords

  • Multi-label classification
  • Supervised learning
  • Interdependence model

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    http://mulan.sourceforge.net.

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Acknowledgment

This work was supported by JSPS KAKENHI Grant Number 15H01704.

Author information

Authors and Affiliations

  1. Sansan, Inc., Tokyo, Japan

    Kosuke Yoshimura

  2. Yahoo Japan Corporation, Tokyo, Japan

    Tomoaki Iwase

  3. University of Tsukuba, Ibaraki, Japan

    Yukino Baba

  4. Kyoto University, Kyoto, Japan

    Hisashi Kashima

  5. RIKEN Center for AIP, Tokyo, Japan

    Hisashi Kashima

Authors
  1. Kosuke Yoshimura
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  2. Tomoaki Iwase
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  3. Yukino Baba
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  4. Hisashi Kashima
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Corresponding author

Correspondence to Kosuke Yoshimura .

Editor information

Editors and Affiliations

  1. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Igor V. Tetko

  2. Institute of Computer Science, Czech Academy of Sciences, Prague 8, Czech Republic

    Dr. Věra Kůrková

  3. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Pavel Karpov

  4. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Prof. Fabian Theis

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Yoshimura, K., Iwase, T., Baba, Y., Kashima, H. (2019). Interdependence Model for Multi-label Classification. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. ICANN 2019. Lecture Notes in Computer Science(), vol 11730. Springer, Cham. https://doi.org/10.1007/978-3-030-30490-4_6

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  • DOI: https://doi.org/10.1007/978-3-030-30490-4_6

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