Robustness of the Quadratic Discriminant Function to correlated and uncorrelated normal training samples
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 vary...
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| Format: | Online |
| Language: | English |
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Springer International Publishing
2016
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4735077/ |
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pubmed-4735077 |
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pubmed-47350772016-02-12 Robustness of the Quadratic Discriminant Function to correlated and uncorrelated normal training samples Adebanji, Atinuke Asamoah-Boaheng, Michael Osei-Tutu, Olivia Research 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 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\delta$$\end{document}δ, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\delta = 1; 2; 3; 4; 5$$\end{document}δ=1;2;3;4;5) on classification accuracy of the QDF using simulated data from three populations (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi _{i}, i=1,2,3$$\end{document}πi,i=1,2,3). 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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\delta$$\end{document}δ 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. Springer International Publishing 2016-02-01 /pmc/articles/PMC4735077/ /pubmed/26877900 http://dx.doi.org/10.1186/s40064-016-1718-3 Text en © Adebanji et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Adebanji, Atinuke Asamoah-Boaheng, Michael Osei-Tutu, Olivia |
| spellingShingle |
Adebanji, Atinuke Asamoah-Boaheng, Michael Osei-Tutu, Olivia Robustness of the Quadratic Discriminant Function to correlated and uncorrelated normal training samples |
| author_facet |
Adebanji, Atinuke Asamoah-Boaheng, Michael Osei-Tutu, Olivia |
| author_sort |
Adebanji, Atinuke |
| title |
Robustness of the Quadratic Discriminant Function to correlated and uncorrelated normal training samples |
| title_short |
Robustness of the Quadratic Discriminant Function to correlated and uncorrelated normal training samples |
| title_full |
Robustness of the Quadratic Discriminant Function to correlated and uncorrelated normal training samples |
| title_fullStr |
Robustness of the Quadratic Discriminant Function to correlated and uncorrelated normal training samples |
| title_full_unstemmed |
Robustness of the Quadratic Discriminant Function to correlated and uncorrelated normal training samples |
| title_sort |
robustness of the quadratic discriminant function to correlated and uncorrelated normal training samples |
| description |
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 (\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$\delta$$\end{document}δ, \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$\delta = 1; 2; 3; 4; 5$$\end{document}δ=1;2;3;4;5)
on classification accuracy of the QDF using simulated data from three populations (\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$\pi _{i}, i=1,2,3$$\end{document}πi,i=1,2,3).
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 \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$\delta$$\end{document}δ 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.
|
| publisher |
Springer International Publishing |
| publishDate |
2016 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4735077/ |
| _version_ |
1613531251042418688 |