Multi-Objective Parameter Selection for Classifiers

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Authors Christoph Müssel, Ludwig Lausser, Markus Maucher, Hans Armin Kestler
Journal/Conference Name Journal of Statistical Software
Paper Category
Paper 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.
Date of publication 2012
Code Programming Language R
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