Processor and method of weighted feature importance estimation
Abstract
A method includes determining a feature importance ranking for each pair of clusters of a plurality of clusters to generate a first plurality of feature importance rankings. The method further includes determining a feature importance ranking between a particular data element and each cluster to generate a second plurality of feature importance rankings. A distance value associated with each pair of clusters of the plurality of clusters is determined to generate a plurality of distance values, and a probability value associated with each data element is determined to generate a plurality of probability values. The method further includes weighting the first plurality of feature importance rankings based on the plurality of distance values to determine a first plurality of weighted feature importance rankings and weighting the second plurality of feature importance rankings based on the plurality of probability values to determine a second plurality of weighted feature importance rankings.
- Inventors:
- Issue Date:
- Research Org.:
- SparkCognition, Inc., Austin, TX (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1735011
- Patent Number(s):
- 10706323
- Application Number:
- 16/559,998
- Assignee:
- SparkCognition, Inc. (Austin, TX)
- Patent Classifications (CPCs):
-
G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- DOE Contract Number:
- FE0031563
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 09/04/2019
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING
Citation Formats
Liebman, Elad. Processor and method of weighted feature importance estimation. United States: N. p., 2020.
Web.
Liebman, Elad. Processor and method of weighted feature importance estimation. United States.
Liebman, Elad. Tue .
"Processor and method of weighted feature importance estimation". United States. https://www.osti.gov/servlets/purl/1735011.
@article{osti_1735011,
title = {Processor and method of weighted feature importance estimation},
author = {Liebman, Elad},
abstractNote = {A method includes determining a feature importance ranking for each pair of clusters of a plurality of clusters to generate a first plurality of feature importance rankings. The method further includes determining a feature importance ranking between a particular data element and each cluster to generate a second plurality of feature importance rankings. A distance value associated with each pair of clusters of the plurality of clusters is determined to generate a plurality of distance values, and a probability value associated with each data element is determined to generate a plurality of probability values. The method further includes weighting the first plurality of feature importance rankings based on the plurality of distance values to determine a first plurality of weighted feature importance rankings and weighting the second plurality of feature importance rankings based on the plurality of probability values to determine a second plurality of weighted feature importance rankings.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Jul 07 00:00:00 EDT 2020},
month = {Tue Jul 07 00:00:00 EDT 2020}
}
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