Results 11 to 20 of about 3,746,451 (299)
Boosted Decision Trees and Applications
EPJ Web of Conferences, 2013 Decision trees are a machine learning technique more and more commonly used in high energy physics, while it has been widely used in the social sciences.
Coadou Yann
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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.
Zhu, Bingzhao, Shoaran, Mahsa
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Machine Learning, 1993 Quinlan and Rivest have suggested a decision-tree inference method using the Minimum Description Length idea. We show that there is an error in their derivation of message lengths, which fortunately has no effect on the final inference. We further suggest two improvements to their coding techniques, one removing an inefficiency in the description of ...
Chris S. Wallace, Jon D. Patrick
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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 Irsoy, Ethem Alpaydin
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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 ...
Olcay Taner Yildiz, Ethem Alpaydin
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CoRR, 2022 Focusing on Random Forests, we propose a multi-armed contextual bandit recommendation framework for feature-based selection of a single shallow tree of the learned ensemble. The trained system, which works on top of the Random Forest, dynamically identifies a base predictor that is responsible for providing the final output.
Tommaso Aldinucci +3 more
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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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Optimization of decision trees using modified African buffalo algorithm
Journal of King Saud University: Computer and Information Sciences, 2022 Decision tree induction is a simple, however powerful learning and classification tool to discover knowledge from the database. The volume of data in databases is growing to quite large sizes, both in the number of attributes and instances.
Archana R. Panhalkar, Dharmpal D. Doye
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, 2021 This is the first release of Interactive Decision Trees package. It contains Python modules that enable the user to interact with the Decision Tree by creating new composite variables, grouping the input variables and color code the groups, selecting ...
Pianosi, Francesca +2 more
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FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees [PDF]
Judgment and Decision Making, 2017 Fast-and-frugal trees (FFTs) are simple algorithms that facilitate efficient and accurate decisions based on limited information. But despite their successful use in many applied domains, there is no widely available toolbox that allows anyone to easily ...
Nathaniel D. Phillips +3 more
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