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Predicting sample size required for classification performance

Overview of attention for article published in BMC Medical Informatics and Decision Making, February 2012
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)

Mentioned by

twitter
6 tweeters
patent
3 patents
q&a
1 Q&A thread

Citations

dimensions_citation
87 Dimensions

Readers on

mendeley
396 Mendeley
citeulike
2 CiteULike
Title
Predicting sample size required for classification performance
Published in
BMC Medical Informatics and Decision Making, February 2012
DOI 10.1186/1472-6947-12-8
Pubmed ID
Authors

Rosa L Figueroa, Qing Zeng-Treitler, Sasikiran Kandula, Long H Ngo

Abstract

Supervised learning methods need annotated data in order to generate efficient models. Annotated data, however, is a relatively scarce resource and can be expensive to obtain. For both passive and active learning methods, there is a need to estimate the size of the annotated sample required to reach a performance target.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 396 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 2%
United Kingdom 4 1%
Germany 3 <1%
Canada 2 <1%
Malaysia 2 <1%
Brazil 2 <1%
Sweden 1 <1%
Australia 1 <1%
Switzerland 1 <1%
Other 4 1%
Unknown 370 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 86 22%
Researcher 84 21%
Student > Master 54 14%
Student > Bachelor 32 8%
Other 25 6%
Other 88 22%
Unknown 27 7%
Readers by discipline Count As %
Computer Science 83 21%
Engineering 69 17%
Medicine and Dentistry 50 13%
Agricultural and Biological Sciences 34 9%
Psychology 16 4%
Other 96 24%
Unknown 48 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 04 June 2019.
All research outputs
#1,684,038
of 14,123,193 outputs
Outputs from BMC Medical Informatics and Decision Making
#163
of 1,309 outputs
Outputs of similar age
#13,938
of 120,854 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 1 outputs
Altmetric has tracked 14,123,193 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,309 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has done well, scoring higher than 87% 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 120,854 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 88% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them