FIND:Explainable Framework for Meta-learning
This addresses the problem of transparency and fairness in meta-learning for users needing automated model selection, though it appears incremental as it builds on existing meta-learning techniques.
The paper tackles the lack of explainability in meta-learning by proposing FIND, an interpretable framework that explains algorithm selection recommendations and performance, with validity demonstrated through experiments.
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.