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Title: Creating ensembles of decision trees through sampling

Abstract

A system for decision tree ensembles that includes a module to read the data, a module to sort the data, a module to evaluate a potential split of the data according to some criterion using a random sample of the data, a module to split the data, and a module to combine multiple decision trees in ensembles. The decision tree method is based on statistical sampling techniques and includes the steps of reading the data; sorting the data; evaluating a potential split according to some criterion using a random sample of the data, splitting the data, and combining multiple decision trees in ensembles.

Inventors:
;
Publication Date:
Research Org.:
The Regents of the University of California, Oakland, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1175481
Patent Number(s):
6,938,049
Application Number:
10/167,892
Assignee:
The Regents of the University of California (Oakland, CA) OSTI
DOE Contract Number:  
W-7405-ENG-48
Resource Type:
Patent
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Kamath, Chandrika, and Cantu-Paz, Erick. Creating ensembles of decision trees through sampling. United States: N. p., 2005. Web.
Kamath, Chandrika, & Cantu-Paz, Erick. Creating ensembles of decision trees through sampling. United States.
Kamath, Chandrika, and Cantu-Paz, Erick. Tue . "Creating ensembles of decision trees through sampling". United States. https://www.osti.gov/servlets/purl/1175481.
@article{osti_1175481,
title = {Creating ensembles of decision trees through sampling},
author = {Kamath, Chandrika and Cantu-Paz, Erick},
abstractNote = {A system for decision tree ensembles that includes a module to read the data, a module to sort the data, a module to evaluate a potential split of the data according to some criterion using a random sample of the data, a module to split the data, and a module to combine multiple decision trees in ensembles. The decision tree method is based on statistical sampling techniques and includes the steps of reading the data; sorting the data; evaluating a potential split according to some criterion using a random sample of the data, splitting the data, and combining multiple decision trees in ensembles.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2005},
month = {8}
}

Patent:

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