Results 21 to 30 of about 3,593,349 (290)
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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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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Structure Information in Decision Trees and Similar Formalisms
, 2007 In attempting to address real-life decision problems, where uncertainty about input data prevails, some kind of representation of imprecise information is important and several have been proposed over the years. In particular, first-order representations
Danielsson, Matd +2 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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, 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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A Bi-criteria optimization model for adjusting the decision tree parameters
Kuwait Journal of Science, 2022 Decision trees play a very important role in knowledge representation because of its simplicity and self-explanatory nature. We study the optimization of the parameters of the decision trees to find a shorter as well as more accurate decision tree ...
Mohammad Azad, Mikhail Moshkov
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