2018/07/22 by Jiaoyan Chen, Freddy Lecue, Chen, Jiaoyan +8 · 3 citations
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Scientific Computing and Data Management #Topic Modeling #cs.AI #cs.LG
paper · pdf · doi:10.48550/arxiv.1807.08372
Accepted by International Conference on Principles of Knowledge Representation and Reasoning, 2018
arxiv created 2018/07/22 · openalex publication_date 2018/07/22 · arxiv updated 2018/07/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.