| 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. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Ghana | 1 | 11% |
| Unknown | 8 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Lecturer | 1 | 11% |
| Student > Doctoral Student | 1 | 11% |
| Student > Bachelor | 1 | 11% |
| Student > Ph. D. Student | 1 | 11% |
| Student > Master | 1 | 11% |
| Other | 0 | 0% |
| Unknown | 4 | 44% |
| Readers by discipline | Count | As % |
|---|---|---|
| Mathematics | 1 | 11% |
| Computer Science | 1 | 11% |
| Engineering | 1 | 11% |
| Unknown | 6 | 67% |
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
#15,377,977
of 22,877,793 outputs
Outputs from SpringerPlus
#935
of 1,850 outputs
Outputs of similar age
#233,950
of 397,467 outputs
Outputs of similar age from SpringerPlus
#80
of 221 outputs
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