Multi-Objective Parameter Selection for Classifiers

By Christoph Müssel, Ludwig Maximilian Lausser, Markus Maucher, Hans A. Kestler. 2012

54 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Control and Systems Engineering

Citations in the corpus

Strongly agree or strongly disagree?: Rating features in Support Vector Machines (2016, background): “It is natural to consider the problem of simultaneous optimization of both accuracy and interpretability, see for instance Hooker and Williams (2012), Müssel et al. (2012).”