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International Workshop on New Frontiers in Mining Complex Patterns

NFMCP 2013: New Frontiers in Mining Complex Patterns pp 35–50Cite as

A Classification Based Scoring Function for Continuous Time Bayesian Network Classifiers

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8399))

Abstract

Continuous time Bayesian network classifiers are designed for analyzing multivariate streaming data when time duration of events matters. New continuous time Bayesian network classifiers are introduced while their conditional log-likelihood scoring function is developed. A learning algorithm, combining conditional log-likelihood with Bayesian parameter estimation is developed. Classification accuracies achieved on synthetic data by continuous time and dynamic Bayesian network classifiers are compared. Results show that conditional log-likelihood scoring combined with Bayesian parameter estimation outperforms marginal log-likelihood scoring in terms of classification accuracy. Continuous time Bayesian network classifiers are applied to post-stroke rehabilitation.

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Notes

  1. 1.

    This definition differs from the one proposed in [25]. In fact, this definition does not require the CTBNC graph to be connected. Therefore, it allows structural learning algorithms to naturally perform feature selection.

  2. 2.

    DTW and Open End DTW (OE-DTW) obtained \(0.99\) accuracy values over the \(2\) class data set, while DTW obtained \(0.88\) and OE-DTW obtained \(0.87\) accuracy values over the \(6\) class data set [27].

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Acknowledgements

The authors would like to acknowledge the many helpful suggestions of the anonymous reviewers, who helped to improve the paper clarity and quality. The authors would like to thank Project Automation S.p.A. for funding the Ph.D. programme of Daniele Codecasa.

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Authors and Affiliations

  1. DISCo, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126, Milano, Italy

    Daniele Codecasa & Fabio Stella

Authors
  1. Daniele Codecasa
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  2. Fabio Stella
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Corresponding author

Correspondence to Fabio Stella .

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. Rende, Italy

    Elio Masciari

  6. Department of Computer Science, University of North Carolina, Charlotte, North Carolina, USA

    Zbigniew W. Ras

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Codecasa, D., Stella, F. (2014). A Classification Based Scoring Function for Continuous Time Bayesian Network Classifiers. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2013. Lecture Notes in Computer Science(), vol 8399. Springer, Cham. https://doi.org/10.1007/978-3-319-08407-7_3

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  • DOI: https://doi.org/10.1007/978-3-319-08407-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08406-0

  • Online ISBN: 978-3-319-08407-7

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