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Joint German/Austrian Conference on Artificial Intelligence (Künstliche Intelligenz)

KI 2017: KI 2017: Advances in Artificial Intelligence pp 170–177Cite as

Evolving Kernel PCA Pipelines with Evolution Strategies

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10505))

Abstract

This paper introduces an evolutionary tuning approach for a pipeline of preprocessing methods and kernel principal component analysis (PCA) employing evolution strategies (ES). A simple (1+1)-ES adapts the imputation method, various preprocessing steps like normalization and standardization, and optimizes the parameters of kernel PCA. A small experimental study on a benchmark data set with missing values demonstrates that the evolutionary kernel PCA pipeline can be tuned with relatively few optimization steps, which makes evolutionary tuning applicable to scenarios with very large data sets.

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Notes

  1. 1.

    type numpy.nan in Python.

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Author information

Authors and Affiliations

  1. Computational Intelligence Group, Department of Computer Science, University of Oldenburg, Oldenburg, Germany

    Oliver Kramer

Authors
  1. Oliver Kramer
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Corresponding author

Correspondence to Oliver Kramer .

Editor information

Editors and Affiliations

  1. Fakultät für Informatik, Technische Universität Dortmund, Dortmund, Germany

    Gabriele Kern-Isberner

  2. FB Informatik, TU Darmstadt, Darmstadt, Hessen, Germany

    Johannes Fürnkranz

  3. FB Informatik, Universität Koblenz, Koblenz, Rheinland-Pfalz, Germany

    Matthias Thimm

A Data Sets

A Data Sets

UCI Digits comprises handwritten Digits with \(d=64\). Friedman is a regression problem generated with scikit-learn and \(d=500\). The Wind data set is based on spatio-temporal time series data from the National Renewable Energy Laboratory (NREL) comprising 11 three MW turbines for three years in a 10-minute resolution, resulting in \(d=11\) dimensions. The Image data set contains image segmentation data with \(d=19\) dimensions.

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Kramer, O. (2017). Evolving Kernel PCA Pipelines with Evolution Strategies. In: Kern-Isberner, G., Fürnkranz, J., Thimm, M. (eds) KI 2017: Advances in Artificial Intelligence. KI 2017. Lecture Notes in Computer Science(), vol 10505. Springer, Cham. https://doi.org/10.1007/978-3-319-67190-1_13

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  • DOI: https://doi.org/10.1007/978-3-319-67190-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67189-5

  • Online ISBN: 978-3-319-67190-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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