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 Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1464129
- Patent Number(s):
- 10031965
- Application Number:
- 14/541,618
- Assignee:
- Battelle Memorial Institute (Richland, WA)
- Patent Classifications (CPCs):
-
G - PHYSICS G06 - COMPUTING G06F - ELECTRIC DIGITAL DATA PROCESSING
- 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 = {Tue Jul 24 00:00:00 EDT 2018},
month = {Tue Jul 24 00:00:00 EDT 2018}
}
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