Decision trees: from efficient prediction to responsible AI [PDF]
Frontiers in Artificial Intelligence, 2023 This article provides a birds-eye view on the role of decision trees in machine learning and data science over roughly four decades. It sketches the evolution of decision tree research over the years, describes the broader context in which the research ...
Hendrik Blockeel +6 more
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On Complexity of Deterministic and Nondeterministic Decision Trees for Conventional Decision Tables from Closed Classes [PDF]
Entropy, 2023 In this paper, we consider classes of conventional decision tables closed relative to the removal of attributes (columns) and changing decisions assigned to rows.
Azimkhon Ostonov, Mikhail Moshkov
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Decision Trees for Binary Subword-Closed Languages [PDF]
Entropy, 2023 In this paper, we study arbitrary subword-closed languages over the alphabet {0,1} (binary subword-closed languages). For the set of words L(n) of the length n belonging to a binary subword-closed language L, we investigate the depth of the decision ...
Mikhail Moshkov
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Comparative Analysis of Deterministic and Nondeterministic Decision Trees for Decision Tables from Closed Classes [PDF]
EntropyIn this paper, we consider classes of decision tables with many-valued decisions closed under operations of the removal of columns, the changing of decisions, the permutation of columns, and the duplication of columns.
Azimkhon Ostonov, Mikhail Moshkov
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Applications of Depth Minimization of Decision Trees Containing Hypotheses for Multiple-Value Decision Tables [PDF]
Entropy, 2023 In this research, we consider decision trees that incorporate standard queries with one feature per query as well as hypotheses consisting of all features’ values.
Mohammad Azad, Mikhail Moshkov
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Data-Driven EEG Band Discovery with Decision Trees [PDF]
Sensors, 2022 Electroencephalography (EEG) is a brain imaging technique in which electrodes are placed on the scalp. EEG signals are commonly decomposed into frequency bands called delta, theta, alpha, and beta.
Shawhin Talebi +4 more
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On Tackling Explanation Redundancy in Decision Trees [PDF]
Journal of Artificial Intelligence Research, 2022 Decision trees (DTs) epitomize the ideal of interpretability of machine learning (ML) models. The interpretability of decision trees motivates explainability approaches by so-called intrinsic interpretability, and it is at the core of recent proposals
Yacine Izza +2 more
semanticscholar +1 more source
On Computing Probabilistic Explanations for Decision Trees [PDF]
Neural Information Processing Systems, 2022 Formal XAI (explainable AI) is a growing area that focuses on computing explanations with mathematical guarantees for the decisions made by ML models. Inside formal XAI, one of the most studied cases is that of explaining the choices taken by decision ...
M. Arenas +3 more
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A Review and Experimental Comparison of Multivariate Decision Trees
IEEE Access, 2021 Decision trees are popular as stand-alone classifiers or as base learners in ensemble classifiers. Mostly, this is due to decision trees having the advantage of being easy to explain.
Leonardo Canete-Sifuentes +2 more
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Constraint Enforcement on Decision Trees: A Survey
ACM Computing Surveys, 2022 Decision trees have the particularity of being machine learning models that are visually easy to interpret and understand. Therefore, they are primarily suited for sensitive domains like medical diagnosis, where decisions need to be explainable. However,
Géraldin Nanfack +2 more
semanticscholar +1 more source