mlsanityData to 7 Oct 2026

FIND:Explainable Framework for Meta-learning

arXiv 2205.10362 ↗First posted 20 May 2022cs.LG cs.AIcited by 1

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Abstract (arXiv)

Meta-learning is used to efficiently enable the automatic selection of machine learning models by combining data and prior knowledge. Since the traditional meta-learning technique lacks explainability, as well as shortcomings in terms of transparency and fairness, achieving explainability for meta-learning is crucial. This paper proposes FIND, an interpretable meta-learning framework that not only can explain the recommendation results of meta-learning algorithm selection, but also provide a more complete and accurate explanation of the recommendation algorithm's performance on specific datasets combined with business scenarios. The validity and correctness of this framework have been demonstrated by extensive experiments.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 22 of its 43 references that are in the map. First posted on arXiv 20 May 2022.

Builds on

22 of 43 references.

References · 22

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10 more
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Citing papers per year: 2023: 1.

1 paper.

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