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Title: Data object classification using feature generation through crowdsourcing

Abstract

In a computing device that implements a data object classification tool, a method for classifying data may include detecting change in spatial coordinates for each of at least two of a set of data objects within a canvas space. Each of the data objects may be associated with a vector of features. A rule set may be generated based on the vector of features associated with each of the at least two data objects. The rule set may use feature(s) that explain the changed spatial coordinates. The data objects may be selectively rearranged within the canvas space by applying the generated rule set to any remaining data objects among the set of data objects so as to assign spatial coordinates to the remaining objects. For each of the data objects, the spatial coordinates may be stored as new semantic feature(s) within the vector of features for that data object.

Inventors:
Issue Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1464129
Patent Number(s):
10,031,965
Application Number:
14/541,618
Assignee:
Battelle Memorial Institute (Richland, WA)
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Patent
Resource Relation:
Patent File Date: 2014 Nov 14
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Hodas, Nathan O. Data object classification using feature generation through crowdsourcing. United States: N. p., 2018. Web.
Hodas, Nathan O. Data object classification using feature generation through crowdsourcing. United States.
Hodas, Nathan O. Tue . "Data object classification using feature generation through crowdsourcing". United States. https://www.osti.gov/servlets/purl/1464129.
@article{osti_1464129,
title = {Data object classification using feature generation through crowdsourcing},
author = {Hodas, Nathan O.},
abstractNote = {In a computing device that implements a data object classification tool, a method for classifying data may include detecting change in spatial coordinates for each of at least two of a set of data objects within a canvas space. Each of the data objects may be associated with a vector of features. A rule set may be generated based on the vector of features associated with each of the at least two data objects. The rule set may use feature(s) that explain the changed spatial coordinates. The data objects may be selectively rearranged within the canvas space by applying the generated rule set to any remaining data objects among the set of data objects so as to assign spatial coordinates to the remaining objects. For each of the data objects, the spatial coordinates may be stored as new semantic feature(s) within the vector of features for that data object.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {7}
}

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