Results 11 to 20 of about 4,859,475 (319)
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
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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
Decision Rules Derived from Optimal Decision Trees with Hypotheses
Entropy, 2021 Conventional decision trees use queries each of which is based on one attribute. In this study, we also examine decision trees that handle additional queries based on hypotheses.
Mohammad Azad +4 more
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Predicting Credit Scores with Boosted Decision Trees
Forecasting, 2022 Credit scoring models help lenders decide whether to grant or reject credit to applicants. This paper proposes a credit scoring model based on boosted decision trees, a powerful learning technique that aggregates several decision trees to form a ...
João A. Bastos
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Evolutionary Learning of Interpretable Decision Trees
IEEE Access, 2023 In the last decade, reinforcement learning (RL) has been used to solve several tasks with human-level performance. However, there is a growing demand for interpretable RL, i.e., there is the need to understand how a RL agent works and the rationale of ...
Leonardo L. Custode, Giovanni Iacca
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Multivariate decision trees [PDF]
Machine Learning, 1995 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Carla E. Brodley, Paul E. Utgoff
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Tree in Tree: from Decision Trees to Decision Graphs
, 2021 Decision trees have been widely used as classifiers in many machine learning applications thanks to their lightweight and interpretable decision process. This paper introduces Tree in Tree decision graph (TnT), a framework that extends the conventional decision tree to a more generic and powerful directed acyclic graph.
Zhu, Bingzhao, Shoaran, Mahsa
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Decision trees for regular factorial languages
Array, 2022 In this paper, we study arbitrary regular factorial languages over a finite alphabet Σ. For the set of words L(n)of the length n belonging to a regular factorial language L, we investigate the depth of decision trees solving the recognition and the ...
Mikhail Moshkov
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