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A Dataset Complexity Measure for Analogical Transfer

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Abstract

Analogical transfer consists in leveraging a measure of similarity between two situations to predict the amount of similarity between their outcomes. Acquiring a suitable similarity measure for analogical transfer may be difficult, especially when the data is sparse or when the domain knowledge is incomplete. To alleviate this problem, this paper presents a dataset complexity measure that can be used either to select an optimal similarity measure, or if the similarity measure is given, to perform analogical transfer: among the potential outcomes of a new situation, the most plausible is the one which minimizes the dataset complexity.
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Dates and versions

hal-03029949 , version 1 (29-11-2020)

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  • HAL Id : hal-03029949 , version 1

Cite

Fadi Badra. A Dataset Complexity Measure for Analogical Transfer. International Joint Conference on Artificial Intelligence, Jan 2021, Kyoto, Japan. ⟨hal-03029949⟩
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