4 papers · 1 filter
Local Robustness Quantification for Naive Bayes Classifiers and Generative Forests: a General Approach
Adrián Detavernier, Jasper De Bock
We provide methods for calculating the robustness of the predictions of two types of generative classifiers whose underlying distribution is a Probabilistic Graphical Model (PGM):…
Robustness Quantification and Uncertainty Quantification: Comparing Two Methods for Assessing the Reliability of Classifier Predictions
Adrián Detavernier, Jasper De Bock
We consider two approaches for assessing the reliability of the individual predictions of a classifier: Robustness Quantification (RQ) and Uncertainty Quantification (UQ). We expla…
Robustness and uncertainty: two complementary aspects of the reliability of the predictions of a classifier
Adrián Detavernier, Jasper De Bock
We consider two conceptually different approaches for assessing the reliability of the individual predictions of a classifier: Robustness Quantification (RQ) and Uncertainty Quanti…
Robustness quantification: a new method for assessing the reliability of the predictions of a classifier
Adrián Detavernier, Jasper De Bock
Based on existing ideas in the field of imprecise probabilities, we present a new approach for assessing the reliability of the individual predictions of a generative probabilistic…