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Improving feature selection performance using pairwise pre-evaluation

Overview of attention for article published in BMC Bioinformatics, August 2016
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  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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5 tweeters

Citations

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14 Dimensions

Readers on

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28 Mendeley
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Title
Improving feature selection performance using pairwise pre-evaluation
Published in
BMC Bioinformatics, August 2016
DOI 10.1186/s12859-016-1178-3
Pubmed ID
Authors

Songlu Li, Sejong Oh

Abstract

Biological data such as microarrays contain a huge number of features. Thus, it is necessary to select a small number of novel features to characterize the entire dataset. All combinations of the features subset must be evaluated to produce an ideal feature subset, but this is impossible using currently available computing power. Feature selection or feature subset selection provides a sub-optimal solution within a reasonable amount of time. In this study, we propose an improved feature selection method that uses information based on all the pairwise evaluations for a given dataset. We modify the original feature selection algorithms to use pre-evaluation information. The pre-evaluation captures the quality and interactions between two features. The feature subset should be improved by using the top ranking pairs for two features in the selection process. Experimental results demonstrated that the proposed method improved the quality of the feature subset produced by modified feature selection algorithms. The proposed method can be applied to microarray and other high-dimensional data.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 21%
Student > Ph. D. Student 6 21%
Student > Master 4 14%
Student > Bachelor 3 11%
Student > Doctoral Student 1 4%
Other 5 18%
Unknown 3 11%
Readers by discipline Count As %
Computer Science 10 36%
Engineering 5 18%
Agricultural and Biological Sciences 5 18%
Biochemistry, Genetics and Molecular Biology 1 4%
Mathematics 1 4%
Other 2 7%
Unknown 4 14%

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 29 August 2016.
All research outputs
#6,310,887
of 11,295,096 outputs
Outputs from BMC Bioinformatics
#2,440
of 4,195 outputs
Outputs of similar age
#115,680
of 260,909 outputs
Outputs of similar age from BMC Bioinformatics
#62
of 120 outputs
Altmetric has tracked 11,295,096 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,195 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 39th percentile – i.e., 39% 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 260,909 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 53% of its contemporaries.
We're also able to compare this research output to 120 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.