ExplAIner is a layered first-order logic that expresses major XAI explanation queries over Boolean models with evaluation in the Boolean hierarchy and computation in FP^NP.
On Explaining Decision Trees
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Decision trees (DTs) epitomize what have become to be known as interpretable machine learning (ML) models. This is informally motivated by paths in DTs being often much smaller than the total number of features. This paper shows that in some settings DTs can hardly be deemed interpretable, with paths in a DT being arbitrarily larger than a PI-explanation, i.e. a subset-minimal set of feature values that entails the prediction. As a result, the paper proposes a novel model for computing PI-explanations of DTs, which enables computing one PI-explanation in polynomial time. Moreover, it is shown that enumeration of PI-explanations can be reduced to the enumeration of minimal hitting sets. Experimental results were obtained on a wide range of publicly available datasets with well-known DT-learning tools, and confirm that in most cases DTs have paths that are proper supersets of PI-explanations.
years
2026 3representative citing papers
Machine learning classifiers on initial orbital elements and convolutional neural networks on recurrence plots from short integrations classify long-term ejection of near-Earth asteroids with accuracy comparable to full numerical simulations.
OPTIMUS generates minimal and sufficient concept-based visual explanations for deep classifiers using prime implicant theory to enforce logical sufficiency and minimality.
citing papers explorer
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ExplAIner: A Declarative Query Language for Explaining Classification Models
ExplAIner is a layered first-order logic that expresses major XAI explanation queries over Boolean models with evaluation in the Boolean hierarchy and computation in FP^NP.
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Long-Term Dynamical Evolution and Ejection of Near-Earth Asteroids
Machine learning classifiers on initial orbital elements and convolutional neural networks on recurrence plots from short integrations classify long-term ejection of near-Earth asteroids with accuracy comparable to full numerical simulations.
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OPTIMUS-Prime: Minimal and Sufficient Concept Explanations for Deep Vision Models
OPTIMUS generates minimal and sufficient concept-based visual explanations for deep classifiers using prime implicant theory to enforce logical sufficiency and minimality.