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Multi-label Classification with a Constrained Minimum Cut Model

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Real World Data Mining Applications

Part of the book series: Annals of Information Systems ((AOIS,volume 17))

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Abstract

Multi-label classification has received more attention recently in the fields of data mining and machine learning. Though many approaches have been proposed, the critical issue of how to combine single labels to form a multi-label remains challenging. In this work, we propose a novel multi-label classification approach that each label is represented by two exclusive events: the label is selected or not selected. Then a weighted graph is used to represent all the events and their correlations. The multi-label learning is transformed into finding a constrained minimum cut of the weighted graph. In the experiments, we compare the proposed approach with the state-of-the-art multi-label classifier ML-KNN, and the results show that the new approach is efficient in terms of all the popular metrics used to evaluate multi-label classification performance.

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Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, Oakland University, Rochester, MI, 48309, USA

    Guangzhi Qu & Ishwar Sethi

  2. Anesthesiology Research, School of Medicine, Oakland University, Rochester, MI, 48309, USA

    Craig Hartrick

  3. State Key Laboratory of Software Development Environment, School of Computer Science, Beihang University, Beijing, 100191, China

    Hui Zhang

Authors
  1. Guangzhi Qu
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  2. Ishwar Sethi
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  3. Craig Hartrick
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  4. Hui Zhang
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Corresponding author

Correspondence to Guangzhi Qu .

Editor information

Editors and Affiliations

  1. Research & Advanced Engineering, Ford Motor Company, Dearborn, Michigan, USA

    Mahmoud Abou-Nasr

  2. Universität Hamburg Inst. Wirtschaftsinformatik, Hamburg, Germany

    Stefan Lessmann

  3. Universität Hamburg Inst. Wirtschaftsinformatik, Hamburg, Germany

    Robert Stahlbock

  4. Deptartment of Computer & Information Science, Fordham University, Bronx, New York, USA

    Gary M. Weiss

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Qu, G., Sethi, I., Hartrick, C., Zhang, H. (2015). Multi-label Classification with a Constrained Minimum Cut Model. In: Abou-Nasr, M., Lessmann, S., Stahlbock, R., Weiss, G. (eds) Real World Data Mining Applications. Annals of Information Systems, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-07812-0_5

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