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X Demographics
Mendeley readers
Attention Score in Context
| 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. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 50% |
| Norway | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 61 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 | 58 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 13 | 21% |
| Researcher | 12 | 20% |
| Student > Master | 11 | 18% |
| Student > Doctoral Student | 5 | 8% |
| Professor | 4 | 7% |
| Other | 10 | 16% |
| Unknown | 6 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 12 | 20% |
| Agricultural and Biological Sciences | 10 | 16% |
| Engineering | 6 | 10% |
| Biochemistry, Genetics and Molecular Biology | 5 | 8% |
| Mathematics | 5 | 8% |
| Other | 13 | 21% |
| Unknown | 10 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 27 January 2022.
All research outputs
#3,961,521
of 23,001,641 outputs
Outputs from BMC Bioinformatics
#1,490
of 7,312 outputs
Outputs of similar age
#33,351
of 194,130 outputs
Outputs of similar age from BMC Bioinformatics
#24
of 141 outputs
Altmetric has tracked 23,001,641 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,312 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 79% of its peers.
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 194,130 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 141 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.