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A comprehensive evaluation of ensemble learning for stock-market prediction

Overview of attention for article published in Journal of Big Data, March 2020
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (65th percentile)

Mentioned by

twitter
1 tweeter
patent
1 patent

Citations

dimensions_citation
46 Dimensions

Readers on

mendeley
151 Mendeley
Title
A comprehensive evaluation of ensemble learning for stock-market prediction
Published in
Journal of Big Data, March 2020
DOI 10.1186/s40537-020-00299-5
Authors

Isaac Kofi Nti, Adebayo Felix Adekoya, Benjamin Asubam Weyori

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 151 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 151 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 16%
Student > Master 24 16%
Lecturer 14 9%
Student > Bachelor 13 9%
Student > Doctoral Student 6 4%
Other 23 15%
Unknown 47 31%
Readers by discipline Count As %
Computer Science 56 37%
Engineering 16 11%
Unspecified 6 4%
Economics, Econometrics and Finance 5 3%
Business, Management and Accounting 4 3%
Other 12 8%
Unknown 52 34%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 10 February 2022.
All research outputs
#5,890,188
of 21,131,454 outputs
Outputs from Journal of Big Data
#100
of 303 outputs
Outputs of similar age
#138,767
of 414,546 outputs
Outputs of similar age from Journal of Big Data
#1
of 1 outputs
Altmetric has tracked 21,131,454 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 303 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.7. This one has gotten more attention than average, scoring higher than 66% 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 414,546 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 1 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