Results 11 to 20 of about 979,707 (169)
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 ...
Barnes, Timothy +5 more
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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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Fault Trees, Decision Trees, And Binary Decision Diagrams: A Systematic Comparison [PDF]
Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021), 2021 In reliability engineering, we need to understand system dependencies, cause-effect relations, identify critical components, and analyze how they trigger failures. Three prominent graph models commonly used for these purposes are fault trees (FTs), decision trees (DTs), and binary decision diagrams (BDDs). These models are popular because they are easy
Jimenez-Roa, L.A. +2 more
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IEEE Transactions on Knowledge and Data Engineering, 2017 Uncovering causal relationships in data is a major objective of data analytics. Causal relationships are normally discovered with designed experiments, e.g. randomised controlled trials, which, however are expensive or infeasible to be conducted in many cases.
Jiuyong Li +4 more
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Investigating Evaluation Measures in Ant Colony Algorithms for Learning Decision Tree Classifiers [PDF]
, 2015 Ant-Tree-Miner is a decision tree induction algorithm that is based on the Ant Colony Optimization (ACO) meta- heuristic. Ant-Tree-Miner-M is a recently introduced extension of Ant-Tree-Miner that learns multi-tree classification models.
Abdelbar, Ashraf M. +2 more
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, 2022 Recently proposed budding tree is a decision tree algorithm in which every node is part internal node and part leaf. This allows representing every decision tree in a continuous parameter space, and therefore a budding tree can be jointly trained with backpropagation, like a neural network.
Ozan İrsoy, Ethem Alpaydın
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On the parity complexity measures of Boolean functions [PDF]
, 2010 The parity decision tree model extends the decision tree model by allowing the computation of a parity function in one step. We prove that the deterministic parity decision tree complexity of any Boolean function is polynomially related to the non ...
Beals +10 more
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Hardness and inapproximability results for minimum verification set and minimum path decision tree problems [PDF]
, 2012 Minimization of decision trees is a well studied problem. In this work, we introduce two new problems related to minimization of decision trees. The problems are called minimum verification set (MinVS) and minimum path decision tree (MinPathDT) problems.
Turker, Uraz Cengiz +3 more
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Omnivariate decision trees [PDF]
IEEE Transactions on Neural Networks, 2001 Univariate decision trees at each decision node consider the value of only one feature leading to axis-aligned splits. In a linear multivariate decision tree, each decision node divides the input space into two with a hyperplane. In a nonlinear multivariate tree, a multilayer perceptron at each node divides the input space arbitrarily, at the expense ...
C.T. Yildiz, Ethem Alpaydin
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IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2012 Licensed copy is available on IEEE ...
Manwani, N, Sastry, PS
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