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Decision trees in epidemiological research [PDF]
Emerging Themes in Epidemiology, 2017 Background In many studies, it is of interest to identify population subgroups that are relatively homogeneous with respect to an outcome. The nature of these subgroups can provide insight into effect mechanisms and suggest targets for tailored ...
Ashwini Venkatasubramaniam +5 more
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Tree in Tree: from Decision Trees to Decision Graphs [PDF]
arXiv, 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
arxiv +4 more sources
Decision trees: from efficient prediction to responsible AI
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
doaj +2 more sources
, 1990 This paper describes experiments, on two domains, to investigate the effect of averaging over predictions of multiple decision trees, instead of using a single tree. Other authors have pointed out theoretical and commonsense reasons for preferring the multiple tree approach.
Chris Carter, Suk Wah Kwok
openaire +4 more sources
Bivariate decision trees [PDF]
, 1997 Decision tree methods constitute an important and much used technique for classification problems. When such trees are used in a Datamining and Knowledge Discovery context, ease of interpretation of the resulting trees is an important requirement to be met.
Rob Potharst +2 more
openaire +4 more sources
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
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
doaj +1 more source
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