Method of generating features optimal to a dataset and classifier

Title: Method of generating features optimal to a dataset and classifier

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Abstract

A method of generating features optimal to a particular dataset and classifier is disclosed. A dataset of messages is inputted and a classifier is selected. An algebra of features is encoded. Computable features that are capable of describing the dataset from the algebra of features are selected. Irredundant features that are optimal for the classifier and the dataset are selected.

Inventors:: Bruillard, Paul J.; Gosink, Luke J.; Jarman, Kenneth D.

Issue Date:: 2016-10-18

Research Org.:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1329308

Patent Number(s):: 9,471,871

Application Number:: 14/186,740

Assignee:: BATTELLE MEMORIAL INSTITUTE (Richland, WA) PNNL

DOE Contract Number:: AC05-76RL01830

Resource Type:: Patent

Resource Relation:: Patent File Date: 2014 Feb 21

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING

Citation Formats


                    Bruillard, Paul J., Gosink, Luke J., and Jarman, Kenneth D. Method of generating features optimal to a dataset and classifier.  United States: N. p., 2016. 
        Web.

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                    Bruillard, Paul J., Gosink, Luke J., & Jarman, Kenneth D. Method of generating features optimal to a dataset and classifier.  United States.

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                    Bruillard, Paul J., Gosink, Luke J., and Jarman, Kenneth D. Tue .  
        "Method of generating features optimal to a dataset and classifier".  United States.  https://www.osti.gov/servlets/purl/1329308.

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@article{osti_1329308,

  title        = {Method of generating features optimal to a dataset and classifier},

  author       = {Bruillard, Paul J. and Gosink, Luke J. and Jarman, Kenneth D.},

  abstractNote = {A method of generating features optimal to a particular dataset and classifier is disclosed. A dataset of messages is inputted and a classifier is selected. An algebra of features is encoded. Computable features that are capable of describing the dataset from the algebra of features are selected. Irredundant features that are optimal for the classifier and the dataset are selected.},

  doi          = {},

  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2016},

  month        = {10}

}

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