Mapping Learning Algorithms on Data, a Useful Step for Optimizing Performances and Their Comparison
- 1 Department of Computer Science, University of Naples, Italy
Abstract
In this paper, we propose a novel methodology to map learning algorithms on data (performance map) in order to gain more insights into the distribution of their performances across their parameter space. This methodology provides useful information when selecting a learner's best configuration for the data at hand and it also enhances the comparison of learners across learning contexts. In order to explain the proposed methodology, the study introduces the notions of learning context, performance map, and high-performance function. It then applies these concepts to a variety of learning contexts to show how their use can provide more insights into a learner's behavior and can enhance the comparison of learners across learning contexts. The study is completed by an extensive experimental study describing how the proposed methodology can be applied.
DOI: https://doi.org/10.3844/jcssp.2024.1110.1120
Copyright: © 2024 Filippo Neri. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- Learning Algorithms
- Decision Trees
- Support Vector Machines
- Meta-Optimization of Learners
- Comparing Learning Algorithms
- Performance Maps of Learning Contexts