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Semi-supervised Learning for Multi-target Regression

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New Frontiers in Mining Complex Patterns (NFMCP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8983))

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Abstract

The most common machine learning approach is supervised learning, which uses labeled data for building predictive models. However, in many practical problems, the availability of annotated data is limited due to the expensive, tedious and time-consuming annotation procedure. At the same, unlabeled data can be easily available in large amounts. This is especially pronounced for predictive modelling problems with a structured output space and complex labels.

Semi-supervised learning (SSL) aims to use unlabeled data as an additional source of information in order to build better predictive models than can be learned from labeled data alone. The majority of work in SSL considers the simple tasks of classification and regression where the output space consists of a single variable. Much less work has been done on SSL for structured output prediction.

In this study, we address the task of multi-target regression (MTR), a type of structured output prediction, where the output space consists of multiple numerical values. Our main objective is to investigate whether we can improve over supervised methods for MTR by using unlabeled data. We use ensembles of predictive clustering trees in a self-training fashion: the most reliable predictions (passing a reliability threshold) on unlabeled data are iteratively used to re-train the model. We use the variance of the ensemble models’ predictions as an indicator of the reliability of predictions. Our results provide a proof-of-concept: The use of unlabeled data improves the predictive performance of ensembles for multi-target regression, but further efforts are needed to automatically select the optimal threshold for the reliability of predictions.

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Acknowledgments

We acknowledge the financial support of the Slovenian Research Agency, via the grant P2-0103 and a young researcher grant to the first author, and the European Commission, via the grants ICT-2013-612944 MAESTRA and ICT-2013-604102 HBP.

Author information

Authors and Affiliations

  1. Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia

    Jurica Levatić, Dragi Kocev & Sašo Džeroski

  2. Jožef Stefan International Postgraduate School, Ljubljana, Slovenia

    Jurica Levatić & Sašo Džeroski

  3. Department of Informatics, University of Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci & Dragi Kocev

Authors
  1. Jurica Levatić
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  2. Michelangelo Ceci
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  3. Dragi Kocev
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  4. Sašo Džeroski
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Corresponding author

Correspondence to Michelangelo Ceci .

Editor information

Editors and Affiliations

  1. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  2. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  3. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Corrado Loglisci

  4. ICAR-CNR, Rende, Italy

    Giuseppe Manco

  5. ICAR-CNR, Rende, Italy

    Elio Masciari

  6. University of North Carolina, Charlotte, USA and Warsaw University of Technology, Warsaw, Poland

    Zbigniew W. Ras

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Levatić, J., Ceci, M., Kocev, D., Džeroski, S. (2015). Semi-supervised Learning for Multi-target Regression. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2014. Lecture Notes in Computer Science(), vol 8983. Springer, Cham. https://doi.org/10.1007/978-3-319-17876-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-17876-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17875-2

  • Online ISBN: 978-3-319-17876-9

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