Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Christoph Müssel, Ludwig Lausser, Markus Maucher, Hans A. Kestler
Title: Multi-Objective Parameter Selection for Classifiers
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.

Page views:: 3914. Submitted: 2010-08-17. Published: 2012-01-30.
Paper: Multi-Objective Parameter Selection for Classifiers     Download PDF (Downloads: 4364)
Supplements:
TunePareto_2.2.tar.gz: R source package Download (Downloads: 551; 33KB)
v46i05.R: R example code from the paper Download (Downloads: 659; 10KB)
parkinsons.data: Example data in CSV format Download (Downloads: 608; 39KB)
DOI: 10.18637/jss.v046.i05

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.