Abstract
Machine learning explanation can significantly boost machine learning's application in decision making, but the usability of current methods is limited in human-centric explanation, especially for transfer learning, an important machine learning branch that aims at utilizing knowledge from one learning domain (i.e., a pair of dataset and prediction task) to enhance prediction model training in another learning domain. In this paper, we propose an ontology-based approach for human-centric explanation of transfer learning. Three kinds of knowledge-based explanatory evidence, with different granularities, including general factors, particular narrators and core contexts are first proposed and then inferred with both local ontologies and external knowledge bases. The evaluation with US flight data and DBpedia has presented their confidence and availability in explaining the transferability of feature representation in flight departure delay forecasting.
Abstract (translated)
机器学习解释可以显着提高机器学习在决策中的应用，但是当前方法的可用性在以人为中心的解释中受到限制，特别是对于转移学习而言，转移学习是一个重要的机器学习分支，旨在利用来自一个学习领域的知识（即，一对数据集和预测任务）以增强另一学习领域中的预测模型训练。在本文中，我们提出了一种基于本体的方法，以人为中心解释转移学习。首先提出了三种基于知识的解释性证据，具有不同的粒度，包括一般因素，特定的叙述者和核心语境，然后用局部本体和外部知识库推断出来。美国航班数据和DBpedia的评估表明了他们在解释航班起飞延误预报中特征表示的可转移性方面的信心和可用性。
URL
https://arxiv.org/abs/1807.08372
