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An empirical evaluation of hierarchical feature selection methods for classification in bioinformatics datasets with gene ontology-based features

Overview of attention for article published in Artificial Intelligence Review, January 2017
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About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
1 X user
peer_reviews
1 peer review site

Citations

dimensions_citation
28 Dimensions

Readers on

mendeley
43 Mendeley
Title
An empirical evaluation of hierarchical feature selection methods for classification in bioinformatics datasets with gene ontology-based features
Published in
Artificial Intelligence Review, January 2017
DOI 10.1007/s10462-017-9541-y
Authors

Cen Wan, Alex A. Freitas

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 43 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 23%
Student > Master 4 9%
Student > Bachelor 4 9%
Lecturer 3 7%
Student > Postgraduate 2 5%
Other 7 16%
Unknown 13 30%
Readers by discipline Count As %
Computer Science 13 30%
Engineering 6 14%
Business, Management and Accounting 3 7%
Mathematics 1 2%
Biochemistry, Genetics and Molecular Biology 1 2%
Other 5 12%
Unknown 14 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 13 March 2018.
All research outputs
#14,928,316
of 22,961,203 outputs
Outputs from Artificial Intelligence Review
#406
of 699 outputs
Outputs of similar age
#242,871
of 420,107 outputs
Outputs of similar age from Artificial Intelligence Review
#8
of 8 outputs
Altmetric has tracked 22,961,203 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 699 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 39th percentile – i.e., 39% 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 420,107 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one.