Comparing machine learning algorithms by union-free generic depth

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...

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Bibliographic Details
Published in:International journal of approximate reasoning Vol. 169; p. 109166
Main Authors: Blocher, Hannah, Schollmeyer, Georg, Nalenz, Malte, Jansen, Christoph
Format: Journal Article
Language:English
Published: Elsevier Inc 01.06.2024
Subjects:
Algorithm comparison
Benchmarking
Data depth
Non-standard data
Outlier detection
Partial orders
Partial orders
Benchmarking
Algorithm comparison
Outlier detection
Data depth
Non-standard data
ISSN:0888-613X, 1873-4731
Online Access:Get full text
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Summary: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.1
ISSN:0888-613X
1873-4731
DOI:10.1016/j.ijar.2024.109166