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Multi-Objective Parameter Selection for Classifiers

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  • Müssel, Christoph
  • Lausser, Ludwig
  • Maucher, Markus
  • Kestler, Hans A.

Abstract

Setting the free parameters of classifiers to different values can have a profound impact on their performance. For some methods, specialized tuning algorithms have been developed. These approaches mostly tune parameters according to a single criterion, such as the cross-validation error. However, it is sometimes desirable to obtain parameter values that optimize several concurrent - often conflicting - criteria. The TunePareto package provides a general and highly customizable framework to select optimal parameters for classifiers according to multiple objectives. Several strategies for sampling and optimizing parameters are supplied. The algorithm determines a set of Pareto-optimal parameter configurations and leaves the ultimate decision on the weighting of objectives to the researcher. Decision support is provided by novel visualization techniques.

Suggested Citation

  • Müssel, Christoph & Lausser, Ludwig & Maucher, Markus & Kestler, Hans A., 2012. "Multi-Objective Parameter Selection for Classifiers," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 46(i05).
    • Handle: RePEc:jss:jstsof:v:046:i05
    DOI: http://hdl.handle.net/10.18637/jss.v046.i05
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    Cited by:

    1. Ludwig Lausser & Florian Schmid & Lyn-Rouven Schirra & Adalbert F. X. Wilhelm & Hans A. Kestler, 2018. "Rank-based classifiers for extremely high-dimensional gene expression data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(4), pages 917-936, December.
    2. Lagani, Vincenzo & Athineou, Giorgos & Farcomeni, Alessio & Tsagris, Michail & Tsamardinos, Ioannis, 2017. "Feature Selection with the R Package MXM: Discovering Statistically Equivalent Feature Subsets," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 80(i07).
    3. Ludwig Lausser & Robin Szekely & Hans A. Kestler, 2020. "Chained correlations for feature selection," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 871-884, December.

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