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Unbiased bootstrap error estimation for linear discriminant analysis

Overview of attention for article published in EURASIP Journal on Bioinformatics & Systems Biology, October 2014
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  • Above-average Attention Score compared to outputs of the same age (57th percentile)

Mentioned by

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3 tweeters

Citations

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3 Dimensions

Readers on

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3 Mendeley
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Title
Unbiased bootstrap error estimation for linear discriminant analysis
Published in
EURASIP Journal on Bioinformatics & Systems Biology, October 2014
DOI 10.1186/s13637-014-0015-0
Pubmed ID
Authors

Thang Vu, Chao Sima, Ulisses M Braga-Neto, Edward R Dougherty

Abstract

Convex bootstrap error estimation is a popular tool for classifier error estimation in gene expression studies. A basic question is how to determine the weight for the convex combination between the basic bootstrap estimator and the resubstitution estimator such that the resulting estimator is unbiased at finite sample sizes. The well-known 0.632 bootstrap error estimator uses asymptotic arguments to propose a fixed 0.632 weight, whereas the more recent 0.632+ bootstrap error estimator attempts to set the weight adaptively. In this paper, we study the finite sample problem in the case of linear discriminant analysis under Gaussian populations. We derive exact expressions for the weight that guarantee unbiasedness of the convex bootstrap error estimator in the univariate and multivariate cases, without making asymptotic simplifications. Using exact computation in the univariate case and an accurate approximation in the multivariate case, we obtain the required weight and show that it can deviate significantly from the constant 0.632 weight, depending on the sample size and Bayes error for the problem. The methodology is illustrated by application on data from a well-known cancer classification study.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters 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 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Indonesia 1 33%
Germany 1 33%
Unknown 1 33%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 33%
Researcher 1 33%
Student > Master 1 33%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 67%
Mathematics 1 33%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 03 October 2014.
All research outputs
#7,032,376
of 12,457,990 outputs
Outputs from EURASIP Journal on Bioinformatics & Systems Biology
#14
of 51 outputs
Outputs of similar age
#87,418
of 213,118 outputs
Outputs of similar age from EURASIP Journal on Bioinformatics & Systems Biology
#1
of 1 outputs
Altmetric has tracked 12,457,990 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 51 research outputs from this source. They receive a mean Attention Score of 1.7. This one has gotten more attention than average, scoring higher than 72% 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 213,118 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 57% 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