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A memetic approach for training set selection in imbalanced data sets

Overview of attention for article published in International Journal of Machine Learning and Cybernetics, August 2019
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Mentioned by

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1 Facebook page

Citations

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7 Dimensions

Readers on

mendeley
17 Mendeley
Title
A memetic approach for training set selection in imbalanced data sets
Published in
International Journal of Machine Learning and Cybernetics, August 2019
DOI 10.1007/s13042-019-01000-w
Authors

Bahareh Nikpour, Hossein Nezamabadi-pour

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 18%
Researcher 2 12%
Student > Master 2 12%
Professor 2 12%
Other 1 6%
Other 3 18%
Unknown 4 24%
Readers by discipline Count As %
Computer Science 8 47%
Engineering 2 12%
Economics, Econometrics and Finance 1 6%
Mathematics 1 6%
Unknown 5 29%
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 02 September 2019.
All research outputs
#20,578,452
of 23,155,957 outputs
Outputs from International Journal of Machine Learning and Cybernetics
#236
of 480 outputs
Outputs of similar age
#289,298
of 340,585 outputs
Outputs of similar age from International Journal of Machine Learning and Cybernetics
#2
of 2 outputs
Altmetric has tracked 23,155,957 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 480 research outputs from this source. They receive a mean Attention Score of 1.4. This one is in the 1st percentile – i.e., 1% 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 340,585 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one.