↓ Skip to main content
Embed badge What is this page?

Model averaging strategies for structure learning in Bayesian networks with limited data

Overview of attention for article published in BMC Bioinformatics, August 2012
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
1 tweeter

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
41 Mendeley

What is this page?

Summary Twitter Dimensions citations
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Model averaging strategies for structure learning in Bayesian networks with limited data
Published in
BMC Bioinformatics, August 2012
DOI 10.1186/1471-2105-13-s13-s10
Pubmed ID
23320818
Authors

Bradley M Broom, Kim-Anh Do, Devika Subramanian

Abstract

Considerable progress has been made on algorithms for learning the structure of Bayesian networks from data. Model averaging by using bootstrap replicates with feature selection by thresholding is a widely used solution for learning features with high confidence. Yet, in the context of limited data many questions remain unanswered. What scoring functions are most effective for model averaging? Does the bias arising from the discreteness of the bootstrap significantly affect learning performance? Is it better to pick the single best network or to average multiple networks learnt from each bootstrap resample? How should thresholds for learning statistically significant features be selected?

View on publisher site

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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 2%
United States 1 2%
Poland 1 2%
Unknown 38 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 29%
Researcher 6 15%
Student > Master 4 10%
Student > Bachelor 3 7%
Lecturer 3 7%
Other 8 20%
Unknown 5 12%
Readers by discipline Count As %
Computer Science 7 17%
Medicine and Dentistry 5 12%
Engineering 4 10%
Mathematics 4 10%
Psychology 2 5%
Other 14 34%
Unknown 5 12%

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 01 September 2012.
All research outputs
#7,762,554
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#3,175
of 4,576 outputs
Outputs of similar age
#71,001
of 126,410 outputs
Outputs of similar age from BMC Bioinformatics
#22
of 33 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 21st percentile – i.e., 21% 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 126,410 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.

This page is provided by Altmetric.