↓ Skip to main content

Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach

Overview of attention for article published in BioData Mining, November 2016
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
15 Dimensions

Readers on

mendeley
25 Mendeley
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
Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach
Published in
BioData Mining, November 2016
DOI 10.1186/s13040-016-0114-4
Pubmed ID
Authors

Ursula Neumann, Mona Riemenschneider, Jan-Peter Sowa, Theodor Baars, Julia Kälsch, Ali Canbay, Dominik Heider

Abstract

Biomarker discovery methods are essential to identify a minimal subset of features (e.g., serum markers in predictive medicine) that are relevant to develop prediction models with high accuracy. By now, there exist diverse feature selection methods, which either are embedded, combined, or independent of predictive learning algorithms. Many preceding studies showed the defectiveness of single feature selection results, which cause difficulties for professionals in a variety of fields (e.g., medical practitioners) to analyze and interpret the obtained feature subsets. Whereas each of these methods is highly biased, an ensemble feature selection has the advantage to alleviate and compensate for such biases. Concerning the reliability, validity, and reproducibility of these methods, we examined eight different feature selection methods for binary classification datasets and developed an ensemble feature selection system. By using an ensemble of feature selection methods, a quantification of the importance of the features could be obtained. The prediction models that have been trained on the selected features showed improved prediction performance.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 32%
Student > Doctoral Student 5 20%
Student > Master 3 12%
Researcher 3 12%
Student > Bachelor 1 4%
Other 3 12%
Unknown 2 8%
Readers by discipline Count As %
Computer Science 10 40%
Medicine and Dentistry 3 12%
Nursing and Health Professions 2 8%
Biochemistry, Genetics and Molecular Biology 2 8%
Environmental Science 2 8%
Other 4 16%
Unknown 2 8%

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 21 November 2016.
All research outputs
#3,934,857
of 8,664,483 outputs
Outputs from BioData Mining
#135
of 196 outputs
Outputs of similar age
#127,303
of 296,043 outputs
Outputs of similar age from BioData Mining
#5
of 11 outputs
Altmetric has tracked 8,664,483 research outputs across all sources so far. This one has received more attention than most of these and is in the 53rd percentile.
So far Altmetric has tracked 196 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one is in the 31st percentile – i.e., 31% 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 296,043 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 55% of its contemporaries.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.