In the context of post-hoc interpretability, this paper addresses the task of explaining the prediction of a classifier, considering the case where no information is available, neither on the classifier itself, nor on the processed data (neither the training nor the test data). It proposes an instance-based approach whose principle consists in determining the minimal changes needed to alter a prediction: given a data point whose classification must be explained, the proposed method consists in identifying a close neighbour classified differently, where the closeness definition integrates a sparsity constraint. This principle is implemented using observation generation in the Growing Spheres algorithm. Experimental results on two datasets illustrate the relevance of the proposed approach that can be used to gain knowledge about the classifier.
Inverse Classification for Comparison-based Interpretability in Machine Learning
Thibault Laugel,Marie-Jeanne Lesot,C. Marsala,X. Renard,Marcin Detyniecki
Published 2017 in arXiv.org
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- Publication year
2017
- Venue
arXiv.org
- Publication date
2017-12-22
- Fields of study
Mathematics, Computer Science
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