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A Probabilistic Approach to Multiple-Instance Learning

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Bioinformatics Research and Applications (ISBRA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10330))

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Abstract

This paper introduced a probabilistic approach to the multiple-instance learning (MIL) problem with two Bayes classification algorithms. The first algorithm, named Instance-Vote, provides a simple approach for posterior probability estimation. The second algorithm, Embedded Kernel Density Estimation (EKDE), enables data visualization during classification. Both algorithms were evaluated using MUSK benchmark data sets and the results are highly competitive with existing methods.

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Acknowledgements

This work was supported by the Department of Computer and Information Science, University of Mississippi.

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Authors and Affiliations

  1. Department of Computer and Information Science, The University of Mississippi, University, MS, 38677, USA

    Silu Zhang, Yixin Chen & Dawn Wilkins

Authors
  1. Silu Zhang
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  2. Yixin Chen
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  3. Dawn Wilkins
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Corresponding author

Correspondence to Silu Zhang .

Editor information

Editors and Affiliations

  1. Georgia State University, Atlanta, Georgia, USA

    Zhipeng Cai

  2. University of Texas at Dallas, Richardson, Texas, USA

    Ovidiu Daescu

  3. Central South University, Changsha, China

    Min Li

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Zhang, S., Chen, Y., Wilkins, D. (2017). A Probabilistic Approach to Multiple-Instance Learning. In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_30

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  • DOI: https://doi.org/10.1007/978-3-319-59575-7_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59574-0

  • Online ISBN: 978-3-319-59575-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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