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A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data

Overview of attention for article published in BMC Bioinformatics, July 2009
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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 (91st percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

patent
7 patents

Citations

dimensions_citation
847 Dimensions

Readers on

mendeley
780 Mendeley
citeulike
6 CiteULike
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Title
A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data
Published in
BMC Bioinformatics, July 2009
DOI 10.1186/1471-2105-10-213
Pubmed ID
Authors

Bjoern H Menze, B Michael Kelm, Ralf Masuch, Uwe Himmelreich, Peter Bachert, Wolfgang Petrich, Fred A Hamprecht

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 780 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 <1%
United Kingdom 3 <1%
Germany 2 <1%
Switzerland 2 <1%
Netherlands 2 <1%
South Africa 2 <1%
Austria 1 <1%
Australia 1 <1%
France 1 <1%
Other 2 <1%
Unknown 757 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 149 19%
Student > Master 136 17%
Researcher 93 12%
Student > Bachelor 58 7%
Student > Doctoral Student 41 5%
Other 95 12%
Unknown 208 27%
Readers by discipline Count As %
Computer Science 113 14%
Engineering 96 12%
Agricultural and Biological Sciences 86 11%
Biochemistry, Genetics and Molecular Biology 34 4%
Earth and Planetary Sciences 29 4%
Other 175 22%
Unknown 247 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 15 June 2023.
All research outputs
#2,578,037
of 23,592,647 outputs
Outputs from BMC Bioinformatics
#782
of 7,401 outputs
Outputs of similar age
#9,221
of 112,144 outputs
Outputs of similar age from BMC Bioinformatics
#6
of 34 outputs
Altmetric has tracked 23,592,647 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 7,401 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 89% 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 112,144 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.