Statistical Comparisons of Classifiers by Generalized Stochastic Dominance

Jansen, Christoph and Nalenz, Malte and Schollmeyer, Georg and Augustin, Thomas (2023) Statistical Comparisons of Classifiers by Generalized Stochastic Dominance. Journal of Machine Learning Research, 24. ISSN 1532-4435

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Abstract

Although being a crucial question for the development of machine learning algorithms, there is still no consensus on how to compare classifiers over multiple data sets with respect to several criteria. Every comparison framework is confronted with (at least) three fundamental challenges: the multiplicity of quality criteria, the multiplicity of data sets and the randomness of the selection of data sets. In this paper, we add a fresh view to the vivid debate by adopting recent developments in decision theory. Based on so-called preference systems, our framework ranks classifiers by a generalized concept of stochastic dominance, which powerfully circumvents the cumbersome, and often even self-contradictory, reliance on aggregates. Moreover, we show that generalized stochastic dominance can be operationalized by solving easy-to-handle linear programs and moreover statistically tested employing an adapted two-sample observation-randomization test. This yields indeed a powerful framework for the statistical comparison of classifiers over multiple data sets with respect to multiple quality criteria simultaneously. We illustrate and investigate our framework in a simulation study and with a set of standard benchmark data sets.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Machine Learning Research
Uncontrolled Keywords:
Research Output Funding/no_not_funded
Subjects:
?? no - not fundednoartificial intelligencesoftwarestatistics and probabilitycontrol and systems engineering ??
ID Code:
221173
Deposited By:
Deposited On:
06 Jun 2024 13:55
Refereed?:
Yes
Published?:
Published
Last Modified:
07 Jun 2024 03:10