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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 +2 more
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Multivariate decision trees [PDF]
Machine Learning, 1995 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Carla E Brodley +2 more
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On Complexity of Deterministic and Nondeterministic Decision Trees for Conventional Decision Tables from Closed Classes. [PDF]
Entropy (Basel), 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.
Ostonov A, Moshkov M.
europepmc +2 more sources
Decision Trees for Binary Subword-Closed Languages. [PDF]
Entropy (Basel), 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 ...
Moshkov M.
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Tree in Tree: from Decision Trees to Decision Graphs
CoRR, 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.
Bingzhao Zhu, Mahsa Shoaran
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Comparative Analysis of Deterministic and Nondeterministic Decision Trees for Decision Tables from Closed Classes. [PDF]
Entropy (Basel)In 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.
Ostonov A, Moshkov M.
europepmc +2 more sources
Distributed Data Classification with Coalition-Based Decision Trees and Decision Template Fusion. [PDF]
Entropy (Basel)In distributed data environments, classification tasks are challenged by inconsistencies across independently maintained sources. These environments are inherently characterized by high informational uncertainty.
Kusztal K, Przybyła-Kasperek M.
europepmc +2 more sources
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
doaj +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
doaj +1 more source
Orthogonal decision trees [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2006 This paper introduces orthogonal decision trees that offer an effective way to construct a redundancy-free, accurate, and meaningful representation of large decision-tree-ensembles often created by popular techniques such as bagging, boosting, random forests, and many distributed and data stream mining algorithms.
Hillol Kargupta +2 more
openaire +2 more sources