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An Approach to Reducing Input Parameter Volume for Fault Classifiers

Overview of attention for article published in Machine Intelligence Research, January 2019
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About this Attention Score

  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

twitter
2 X users

Citations

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3 Dimensions

Readers on

mendeley
16 Mendeley
Title
An Approach to Reducing Input Parameter Volume for Fault Classifiers
Published in
Machine Intelligence Research, January 2019
DOI 10.1007/s11633-018-1162-7
Authors

Ann Smith, Fengshou Gu, Andrew D. Ball

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 19%
Student > Master 2 13%
Professor 1 6%
Student > Doctoral Student 1 6%
Professor > Associate Professor 1 6%
Other 0 0%
Unknown 8 50%
Readers by discipline Count As %
Engineering 5 31%
Computer Science 1 6%
Energy 1 6%
Agricultural and Biological Sciences 1 6%
Unknown 8 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 July 2020.
All research outputs
#19,954,338
of 25,385,509 outputs
Outputs from Machine Intelligence Research
#186
of 444 outputs
Outputs of similar age
#321,694
of 446,570 outputs
Outputs of similar age from Machine Intelligence Research
#2
of 10 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 444 research outputs from this source. They receive a mean Attention Score of 2.5. This one is in the 48th percentile – i.e., 48% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 446,570 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 8 of them.