Results 81 to 90 of about 1,054,775 (163)

On Decision Trees, Influences, and Learning Monotone Decision Trees

open access: yes, 2004
In this note we prove that a monotone boolean function computable by a decision tree of size s has average sensitivity at most √ log2 s. As a consequence we show that monotone functions are learnable to constant accuracy under the uniform distribution in time polynomial in their decision tree size.
O'Donnell, Ryan, Servedio, Rocco Anthony
openaire   +3 more sources

Evolution of Decision Trees

open access: yes, 2001
This paper addresses the issue of the induction of orthogonal, oblique and mul­tivariate decision trees. Algorithms pro­posed by other researchers use heuristic, usually based on the information gain con­cept, to induce decision trees greedily. These algorithms are often tailored for a given tree type ( e.g orthogonal), not be­ing able to induce other ...
Llorà Fàbrega, Xavier   +1 more
openaire   +2 more sources

Rockburst prediction in kimberlite using decision tree with incomplete data

open access: yesJournal of Sustainable Mining, 2018
A rockburst is a common engineering geological hazard. In order to predict rockburst potential in kimberlite at an underground diamond mine, a decision tree method was employed.
Yuanyuan Pu, Derek B. Apel, Bob Lingga
doaj  

Selecting a representative decision tree from an ensemble of decision-tree models for fast big data classification

open access: yesJournal of Big Data, 2019
The goal of this paper is to reduce the classification (inference) complexity of tree ensembles by choosing a single representative model out of ensemble of multiple decision-tree models. We compute the similarity between different models in the ensemble
Abraham Itzhak Weinberg, Mark Last
doaj   +1 more source

MAPTree: Beating “Optimal” Decision Trees with Bayesian Decision Trees

open access: yesProceedings of the AAAI Conference on Artificial Intelligence
Decision trees remain one of the most popular machine learning models today, largely due to their out-of-the-box performance and interpretability. In this work, we present a Bayesian approach to decision tree induction via maximum a posteriori inference of a posterior distribution over trees.
Sullivan, Colin   +2 more
openaire   +2 more sources

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