FIND:Explainable Framework for Meta-learning [article]

Xinyue Shao, Hongzhi Wang, Xiao Zhu, Feng Xiong
2022 arXiv   pre-print
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 » ... e 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.
arXiv:2205.10362v2 fatcat:2mo4kyd3onap5av4qyugz4f2ru