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Scalable and efficient multi-label classification for evolving data streams

Overview of attention for article published in Machine Learning, February 2012
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About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

wikipedia
2 Wikipedia pages

Citations

dimensions_citation
121 Dimensions

Readers on

mendeley
126 Mendeley
citeulike
1 CiteULike
Title
Scalable and efficient multi-label classification for evolving data streams
Published in
Machine Learning, February 2012
DOI 10.1007/s10994-012-5279-6
Authors

Jesse Read, Albert Bifet, Geoff Holmes, Bernhard Pfahringer

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 126 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 3 2%
China 2 2%
Spain 2 2%
United Kingdom 2 2%
Germany 1 <1%
Latvia 1 <1%
Poland 1 <1%
Unknown 114 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 21%
Student > Master 24 19%
Researcher 22 17%
Student > Doctoral Student 14 11%
Student > Bachelor 6 5%
Other 17 13%
Unknown 17 13%
Readers by discipline Count As %
Computer Science 83 66%
Engineering 11 9%
Agricultural and Biological Sciences 3 2%
Decision Sciences 2 2%
Social Sciences 2 2%
Other 2 2%
Unknown 23 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 November 2018.
All research outputs
#7,454,951
of 22,790,780 outputs
Outputs from Machine Learning
#281
of 961 outputs
Outputs of similar age
#51,605
of 156,497 outputs
Outputs of similar age from Machine Learning
#3
of 4 outputs
Altmetric has tracked 22,790,780 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 961 research outputs from this source. They receive a mean Attention Score of 4.4. This one has gotten more attention than average, scoring higher than 53% 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 156,497 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.