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Improving SVM classification on imbalanced time series data sets with ghost points

Overview of attention for article published in Knowledge & Information Systems, June 2010
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About this Attention Score

  • Among the highest-scoring outputs from this source (#18 of 218)
  • Good Attention Score compared to outputs of the same age (65th percentile)

Mentioned by

patent
1 patent

Citations

dimensions_citation
38 Dimensions

Readers on

mendeley
34 Mendeley
citeulike
2 CiteULike
connotea
1 Connotea
Title
Improving SVM classification on imbalanced time series data sets with ghost points
Published in
Knowledge & Information Systems, June 2010
DOI 10.1007/s10115-010-0310-3
Authors

Suzan Köknar-Tezel, Longin Jan Latecki

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Russia 1 3%
France 1 3%
United Kingdom 1 3%
Iran, Islamic Republic of 1 3%
Unknown 30 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 21%
Student > Master 7 21%
Researcher 7 21%
Professor > Associate Professor 3 9%
Other 3 9%
Other 7 21%
Readers by discipline Count As %
Computer Science 19 56%
Unspecified 3 9%
Engineering 3 9%
Chemistry 2 6%
Economics, Econometrics and Finance 1 3%
Other 6 18%

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 12 September 2014.
All research outputs
#2,509,810
of 9,400,242 outputs
Outputs from Knowledge & Information Systems
#18
of 218 outputs
Outputs of similar age
#33,420
of 99,013 outputs
Outputs of similar age from Knowledge & Information Systems
#1
of 3 outputs
Altmetric has tracked 9,400,242 research outputs across all sources so far. This one has received more attention than most of these and is in the 58th percentile.
So far Altmetric has tracked 218 research outputs from this source. They receive a mean Attention Score of 1.3. This one has done well, scoring higher than 81% 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 99,013 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 3 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