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Value-cognitive boosting with a support vector machine for cross-project defect prediction

Overview of attention for article published in Empirical Software Engineering, December 2014
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Mentioned by

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1 Facebook page

Citations

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146 Dimensions

Readers on

mendeley
95 Mendeley
Title
Value-cognitive boosting with a support vector machine for cross-project defect prediction
Published in
Empirical Software Engineering, December 2014
DOI 10.1007/s10664-014-9346-4
Authors

Duksan Ryu, Okjoo Choi, Jongmoon Baik

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 95 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Korea, Republic of 1 1%
Unknown 94 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 20 21%
Student > Ph. D. Student 19 20%
Student > Doctoral Student 8 8%
Lecturer 5 5%
Other 4 4%
Other 15 16%
Unknown 24 25%
Readers by discipline Count As %
Computer Science 56 59%
Engineering 7 7%
Agricultural and Biological Sciences 1 1%
Medicine and Dentistry 1 1%
Business, Management and Accounting 1 1%
Other 0 0%
Unknown 29 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 February 2016.
All research outputs
#20,303,950
of 22,842,950 outputs
Outputs from Empirical Software Engineering
#624
of 705 outputs
Outputs of similar age
#277,637
of 331,520 outputs
Outputs of similar age from Empirical Software Engineering
#6
of 12 outputs
Altmetric has tracked 22,842,950 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 705 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 1st percentile – i.e., 1% 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 331,520 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.