论文标题
查找：元学习的可解释框架
FIND:Explainable Framework for Meta-learning
论文作者
论文摘要
元学习用于通过组合数据和先验知识来有效地实现机器学习模型的自动选择。由于传统的元学习技术缺乏解释性，并且在透明度和公平性方面缺点，因此实现元学习的解释性至关重要。本文提出了一个可解释的元学习框架，不仅可以解释元学习算法选择的建议结果，而且还可以对建议算法在特定数据集中的性能和业务场景相结合提供更完整，更准确的解释。广泛的实验已经证明了该框架的有效性和正确性。
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.
