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Title: Discriminant forest classification method and system

Abstract

A hybrid machine learning methodology and system for classification that combines classical random forest (RF) methodology with discriminant analysis (DA) techniques to provide enhanced classification capability. A DA technique which uses feature measurements of an object to predict its class membership, such as linear discriminant analysis (LDA) or Andersen-Bahadur linear discriminant technique (AB), is used to split the data at each node in each of its classification trees to train and grow the trees and the forest. When training is finished, a set of n DA-based decision trees of a discriminant forest is produced for use in predicting the classification of new samples of unknown class.

Inventors:
; ; ; ; ;
Publication Date:
Research Org.:
Lawrence Livermore National Security, LLC (Livermore, CA)
Sponsoring Org.:
USDOE
OSTI Identifier:
1080437
Patent Number(s):
8,306,942
Application Number:
12/436,667
Assignee:
Lawrence Livermore National Security, LLC (Livermore, CA) LLNL
DOE Contract Number:  
AC52-07NA27344
Resource Type:
Patent
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Chen, Barry Y., Hanley, William G., Lemmond, Tracy D., Hiller, Lawrence J., Knapp, David A., and Mugge, Marshall J. Discriminant forest classification method and system. United States: N. p., 2012. Web.
Chen, Barry Y., Hanley, William G., Lemmond, Tracy D., Hiller, Lawrence J., Knapp, David A., & Mugge, Marshall J. Discriminant forest classification method and system. United States.
Chen, Barry Y., Hanley, William G., Lemmond, Tracy D., Hiller, Lawrence J., Knapp, David A., and Mugge, Marshall J. Tue . "Discriminant forest classification method and system". United States. https://www.osti.gov/servlets/purl/1080437.
@article{osti_1080437,
title = {Discriminant forest classification method and system},
author = {Chen, Barry Y. and Hanley, William G. and Lemmond, Tracy D. and Hiller, Lawrence J. and Knapp, David A. and Mugge, Marshall J.},
abstractNote = {A hybrid machine learning methodology and system for classification that combines classical random forest (RF) methodology with discriminant analysis (DA) techniques to provide enhanced classification capability. A DA technique which uses feature measurements of an object to predict its class membership, such as linear discriminant analysis (LDA) or Andersen-Bahadur linear discriminant technique (AB), is used to split the data at each node in each of its classification trees to train and grow the trees and the forest. When training is finished, a set of n DA-based decision trees of a discriminant forest is produced for use in predicting the classification of new samples of unknown class.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2012},
month = {11}
}

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