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Improved shrunken centroid classifiers for high-dimensional class-imbalanced data

Overview of attention for article published in BMC Bioinformatics, February 2013
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Mentioned by

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2 tweeters

Citations

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14 Dimensions

Readers on

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44 Mendeley
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1 CiteULike
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Title
Improved shrunken centroid classifiers for high-dimensional class-imbalanced data
Published in
BMC Bioinformatics, February 2013
DOI 10.1186/1471-2105-14-64
Pubmed ID
Authors

Rok Blagus, Lara Lusa

Abstract

PAM, a nearest shrunken centroid method (NSC), is a popular classification method for high-dimensional data. ALP and AHP are NSC algorithms that were proposed to improve upon PAM. The NSC methods base their classification rules on shrunken centroids; in practice the amount of shrinkage is estimated minimizing the overall cross-validated (CV) error rate.

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Iran, Islamic Republic of 1 2%
United States 1 2%
France 1 2%
Unknown 41 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 25%
Researcher 11 25%
Student > Master 8 18%
Student > Doctoral Student 4 9%
Professor 4 9%
Other 6 14%
Readers by discipline Count As %
Computer Science 11 25%
Agricultural and Biological Sciences 10 23%
Medicine and Dentistry 4 9%
Biochemistry, Genetics and Molecular Biology 4 9%
Mathematics 4 9%
Other 8 18%
Unknown 3 7%

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 23 February 2013.
All research outputs
#9,150,378
of 14,571,674 outputs
Outputs from BMC Bioinformatics
#3,711
of 5,418 outputs
Outputs of similar age
#81,438
of 147,092 outputs
Outputs of similar age from BMC Bioinformatics
#17
of 25 outputs
Altmetric has tracked 14,571,674 research outputs across all sources so far. This one is in the 24th percentile – i.e., 24% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,418 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 23rd percentile – i.e., 23% 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 147,092 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.