Comparing machine learning algorithms by union-free generic depth

Blocher, Hannah and Schollmeyer, Georg and Nalenz, Malte and Jansen, Christoph (2024) Comparing machine learning algorithms by union-free generic depth. International Journal of Approximate Reasoning, 169: 109166. ISSN 0888-613X

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Abstract

We propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions. Despite intensive studies in linear and metric spaces, there is very little discussion on depth functions for non-standard data types such as partial orders. We introduce an adaptation of the well-known simplicial depth to the set of all partial orders, the union-free generic (ufg) depth. Moreover, we utilize our ufg depth for a comparison of machine learning algorithms based on multidimensional performance measures. Concretely, we provide two examples of classifier comparisons on samples of standard benchmark data sets. Our results demonstrate promisingly the wide variety of different analysis approaches based on ufg methods. Furthermore, the examples outline that our approach differs substantially from existing benchmarking approaches, and thus adds a new perspective to the vivid debate on classifier comparison.

Item Type:
Journal Article
Journal or Publication Title:
International Journal of Approximate Reasoning
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1702
Subjects:
?? artificial intelligencetheoretical computer sciencesoftwareapplied mathematics ??
ID Code:
221165
Deposited By:
Deposited On:
06 Jun 2024 13:20
Refereed?:
Yes
Published?:
Published
Last Modified:
07 Jun 2024 03:20