Systematic benchmarking of microarray data feature extraction and classification

Zhang, Jing, Jiang, Tianzi, Liu, Bing, Jiang, Xingpeng and Zhao, Huizhi (2008) Systematic benchmarking of microarray data feature extraction and classification. International Journal of Computer Mathematics, 85 5: 803-811. doi:10.1080/00207160701463237

Author Zhang, Jing
Jiang, Tianzi
Liu, Bing
Jiang, Xingpeng
Zhao, Huizhi
Title Systematic benchmarking of microarray data feature extraction and classification
Journal name International Journal of Computer Mathematics   Check publisher's open access policy
ISSN 0020-7160
Publication date 2008-05
Sub-type Article (original research)
DOI 10.1080/00207160701463237
Volume 85
Issue 5
Start page 803
End page 811
Total pages 9
Place of publication Essex, United Kingdom
Publisher Taylor & Francis
Language eng
Formatted abstract
A combination of microarrays with classification methods is a promising approach to supporting clinical management decisions in oncology. The aim of this paper is to systematically benchmark the role of classification models. Each classification model is a combination of one feature extraction method and one classification method. We consider four feature extraction methods and five classification methods, from which 20 classification models can be derived. The feature extraction methods are t-statistics, non-parametric Wilcoxon statistics, ad hoc signal-to-noise statistics, and principal component analysis (PCA), and the classification methods are Fisher linear discriminant analysis (FLDA), the support vector machine (SVM), the k nearest-neighbour classifier (kNN), diagonal linear discriminant analysis (DLDA), and diagonal quadratic discriminant analysis (DQDA). Twenty randomizations of each of three binary cancer classification problems derived from publicly available datasets are examined. PCA plus FLDA is found to be the optimal classification model.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
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Created: Thu, 20 Oct 2011, 14:54:43 EST by Debra McMurtrie on behalf of Queensland Brain Institute