Results 11 to 20 of about 4,043,886 (303)
A Two-Parameter Fractional Tsallis Decision Tree
Entropy, 2022 Decision trees are decision support data mining tools that create, as the name suggests, a tree-like model. The classical C4.5 decision tree, based on the Shannon entropy, is a simple algorithm to calculate the gain ratio and then split the attributes ...
Jazmín S. De la Cruz-García +2 more
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Improved version of explainable decision forest: Forest-Based Tree [PDF]
IJCI International Journal of Computers and Information, 2023 A Decision Forest is an ensemble learning method that seeks to enhance the predictivity of a single decision tree via training several trees and combining their decisions.
Faten Khalifa +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 Irsoy, 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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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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Multi-test Decision Tree and its Application to Microarray Data Classification [PDF]
, 2014 Objective: The desirable property of tools used to investigate biological data is easy to understand models and predictive decisions. Decision trees are particularly promising in this regard due to their comprehensible nature that resembles the ...
Marcin Czajkowski +5 more
core +1 more source
Decision Tree-Based Ensemble Model for Predicting National Greenhouse Gas Emissions in Saudi Arabia
Applied Sciences, 2023 Greenhouse gas (GHG) emissions must be precisely estimated in order to predict climate change and achieve environmental sustainability in a country.
Muhammad Muhitur Rahman +7 more
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Fault Diagnosis of Induction Motors using Decision Trees [PDF]
, 2006 Decision tree is one of the most effective and widely used methods for building classification model. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining have considered the decision tree method
Yang, Bo-Suk +2 more
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Achieving Verifiable Decision Tree Prediction on Hybrid Blockchains
Entropy, 2023 Machine learning has become increasingly popular in academic and industrial communities and has been widely implemented in various online applications due to its powerful ability to analyze and use data.
Moxuan Fu +5 more
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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
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