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Robustness of the Quadratic Discriminant Function to correlated and uncorrelated normal training samples

Overview of attention for article published in SpringerPlus, February 2016
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Title
Robustness of the Quadratic Discriminant Function to correlated and uncorrelated normal training samples
Published in
SpringerPlus, February 2016
DOI 10.1186/s40064-016-1718-3
Pubmed ID
Authors

Atinuke Adebanji, Michael Asamoah-Boaheng, Olivia Osei-Tutu

Abstract

This study investigates the asymptotic performance of the Quadratic Discriminant Function (QDF) under correlated and uncorrelated normal training samples. This paper specifically examines the effect of correlation, uncorrelation considering different sample size ratios, number of variables and varying group centroid separators ([Formula: see text], [Formula: see text]) on classification accuracy of the QDF using simulated data from three populations ([Formula: see text]). The three populations differs with respect to their mean vector and covariance matrices. The results show the correlated normal distribution exhibits high coefficient of variation as [Formula: see text] increased. The QDF performed better when the training samples were correlated than when they were under uncorrelated normal distribution. The QDF performed better resulting in the reduction in misclassification error rates as group centroid separator increases with non increasing sample size under correlated training samples.

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

Geographical breakdown

Country Count As %
Ghana 1 13%
Unknown 7 88%

Demographic breakdown

Readers by professional status Count As %
Lecturer 1 13%
Student > Doctoral Student 1 13%
Student > Bachelor 1 13%
Student > Ph. D. Student 1 13%
Student > Master 1 13%
Other 0 0%
Unknown 3 38%
Readers by discipline Count As %
Mathematics 1 13%
Computer Science 1 13%
Engineering 1 13%
Unknown 5 63%

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 17 June 2016.
All research outputs
#4,148,229
of 7,909,849 outputs
Outputs from SpringerPlus
#756
of 1,714 outputs
Outputs of similar age
#142,573
of 262,879 outputs
Outputs of similar age from SpringerPlus
#62
of 103 outputs
Altmetric has tracked 7,909,849 research outputs across all sources so far. This one is in the 27th percentile – i.e., 27% of other outputs scored the same or lower than it.
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