Title
Contrastive Explanations with Local Foil Trees
Author
van der Waa, J.
Robeer, M.
van Diggelen, J.
Brinkhuis, M.
Neerincx, M.
Publication year
2018
Abstract
Recent advances in interpretable Machine Learning (iML) and eXplainable AI (XAI) construct explanations based on the importance of features in classification tasks. However, in a high-dimensional feature space this approach may become unfeasible without restraining the set of important features. We propose to utilize the human tendency to ask questions like "Why this output (the fact) instead of that output (the foil)?" to reduce the number of features to those that play a main role in the asked contrast. Our proposed method utilizes locally trained one-versus-all decision trees to identify the disjoint set of rules that causes the tree to classify data points as the foil and not as the fact. In this study we illustrate this approach on three benchmark classification tasks.
Subject
Machine Learning
Models
To reference this document use:
http://resolver.tudelft.nl/uuid:7549e9d2-8153-4f45-86c3-cd4ca200e760
TNO identifier
867986
Source
Proceedings of the ICML Workshop on Human Interpretability in Machine Learning (WHI 2018), Stockholm, Sweden, 37
Document type
conference paper