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Self-configuring Cost-Sensitive Hierarchical Clustering with Recourse

  • Carlos Ansotegui
  • Meinolf Sellmann
  • Kevin TierneyEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11008)

Abstract

We revisit algorithm selection for declarative programming solvers. We introduce two main ideas to improve cost-sensitive hierarchical clustering: First, to augment the portfolio builder with a self-configuration component. And second, we propose that the algorithm selector assesses the confidence level of its own prediction, so that a more defensive recourse action can be used to overturn the original recommendation.

Notes

Acknowledgements

We thank the Paderborn Center for Parallel Computation (PC\(^2\)) for the use of their high throughput cluster and Marius Lindauer for his kind help with the OASC benchmarks.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Carlos Ansotegui
    • 1
  • Meinolf Sellmann
    • 2
  • Kevin Tierney
    • 3
    Email author
  1. 1.University of LleidaLleidaSpain
  2. 2.General Electric, Global Research CenterNiskayunaUSA
  3. 3.Bielefeld UniversityBielefeldGermany

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