Some recent advances in reasoning based on analogical proportions
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Abstract (arXiv)
Analogical proportions compare pairs of items (a, b) and (c, d) in terms of their differences and similarities. They play a key role in the formalization of analogical inference. The paper first discusses how to improve analogical inference in terms of accuracy and in terms of computational cost. Then it indicates the potential of analogical proportions for explanation. Finally, it highlights the close relationship between analogical proportions and multi-valued dependencies, which reveals an unsuspected aspect of the former.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 37 references that are in the map. First posted on arXiv 22 Dec 2022.
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4 of 37 references.
References · 4
- 2021Explanations for Monotonic Classifierscited by 39
- 2014Analogical Dissimilarity: Definition, Algorithms and Two Experiments in Machine Learningcited by 15
- 2020Analogical proportionscited by 13
- 2020Towards Analogy-Based Explanations in Machine Learningcited by 7
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