48 citations · 232 across the 19 of their papers we have counts for
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Feature Necessity & Relevancy in ML Classifier Explanations
Xuanxiang Huang, Martin C. Cooper, Antonio Morgado +2
Given a machine learning (ML) model and a prediction, explanations can be defined as sets of features which are sufficient for the prediction. In some applications, and besides ask…
Efficient Explanations With Relevant Sets
Yacine Izza, Alexey Ignatiev, Nina Narodytska +2
Recent work proposed -relevant inputs (or sets) as a probabilistic explanation for the predictions made by a classifier on a given input. -relevant sets are significant becau…
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay
Joao Marques-Silva, Thomas Gerspacher, Martin C. Cooper +2
Recent work proposed the computation of so-called PI-explanations of Naive Bayes Classifiers (NBCs). PI-explanations are subset-minimal sets of feature-value pairs that are suffici…