Performance of Partial Least Squares + Linear Discriminant Analysis versus k-Nearest Neighbors for Validation Set Classification of Cancer DNA Microarray Data

Joseph L Hagan and Sudesh K Srivastav
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Joseph L Hagan: Department of Pediatrics–Neonatology, Baylor College of Medicine, United States
Sudesh K Srivastav: School of Public Health and Tropical Medicine, Tulane University, United States

Biostatistics and Biometrics Open Access Journal, 2019, vol. 9, issue 1, 9-11

Abstract: The purpose of this study is to systematically compare the performance of Partial Least Squares + Linear Discriminant Analysis (PLS+LDA) with k-nearest neighbors (KNN) for validation set classification of cancer DNA microarray data. Nine different cancer microarray datasets were analyzed to obtain the optimal PLS+LDA and KNN classifiers, which were then compared in terms of the misclassification rates in the validation set. Additionally, the Singh prostate cancer dataset was resampled via bootstrapping for simulation studies of the effect of class imbalance and sample size on the two supervised learning methods’ misclassification rates. Across the 9 cancer datasets, PLS+LDA had a significantly lower validation set misclassification rate than KNN after controlling for classifier evaluation method (p=0.034). After controlling for supervised learning method, the estimated validation set misclassification rate of classifiers evaluated via bootstrap sampling was 2.9% higher (p

Keywords: Biometrics Open Access Journal; Biostatistics and Biometrics; Biostatistics and Biometrics Open Access Journal; Open Access Journals; biometrics journal; biometrics articles; biometrics journal reference; biometrics journal impact factor; biometrics and biostatistics journal impact factor; journal of biometrics; open access juniper publishers; juniper publishers reivew (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:adp:jbboaj:v:9:y:2019:i:1:p:9-11

DOI: 10.19080/BBOAJ.2019.09.555752

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