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:
- Issue Date:
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1080437
- Patent Number(s):
- 8306942
- Application Number:
- 12/436,667
- Assignee:
- Lawrence Livermore National Security, LLC (Livermore, CA)
- Patent Classifications (CPCs):
-
G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- 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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