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International Computing and Combinatorics Conference

COCOON 2019: Computing and Combinatorics pp 425–436Cite as

APTER: Aggregated Prognosis Through Exponential Re-weighting

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11653))

Abstract

This paper considers the task of learning how to make a prognosis of a patient based on his/her micro-array expression levels. The method is an application of the aggregation method as recently proposed in the literature on theoretical machine learning, and excels in its computational convenience and capability to deal with high-dimensional data. This paper gives a formal analysis of the method, yielding rates of convergence similar to what traditional techniques obtain, while it is shown to cope well with an exponentially large set of features. Those results are supported by numerical simulations on a range of publicly available survival-micro-array data sets. It is empirically found that the proposed technique combined with a recently proposed pre-processing technique gives excellent performances. All used software files and data sets are available on the authors’ website http://user.it.uu.se/~liuya610/index.html.

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Notes

  1. 1.

    ‘The Median Isn’t the Message’ as in http://www.prognosis.org/what_does_it_mean.php.

  2. 2.

    The software is available at http://user.it.uu.se/~liuya610/index.html.

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Author information

Authors and Affiliations

  1. Teesside University, Middlesbrough, UK

    Yang Liu

  2. Uppsala University, Uppsala, UK

    Kristiaan Pelckmans

Authors
  1. Yang Liu
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  2. Kristiaan Pelckmans
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Corresponding author

Correspondence to Yang Liu .

Editor information

Editors and Affiliations

  1. The University of Texas at Dallas, Richardson, TX, USA

    Ding-Zhu Du

  2. Xidian University, Xi'an, China

    Zhenhua Duan

  3. Xidian University, Xi'an, China

    Cong Tian

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Liu, Y., Pelckmans, K. (2019). APTER: Aggregated Prognosis Through Exponential Re-weighting. In: Du, DZ., Duan, Z., Tian, C. (eds) Computing and Combinatorics. COCOON 2019. Lecture Notes in Computer Science(), vol 11653. Springer, Cham. https://doi.org/10.1007/978-3-030-26176-4_35

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  • DOI: https://doi.org/10.1007/978-3-030-26176-4_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26175-7

  • Online ISBN: 978-3-030-26176-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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