Maximally Invariant Data Perturbation as Explanation

19 Jun 2018Satoshi HaraKouichi IkenoTasuku SomaTakanori Maehara

While several feature scoring methods are proposed to explain the output of complex machine learning models, most of them lack formal mathematical definitions. In this study, we propose a novel definition of the feature score using the maximally invariant data perturbation, which is inspired from the idea of adversarial example... (read more)

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