A novel feature extraction approach for microarray data based on multi-algorithm fusion

Bioinformation
Zhu Jiang, Rong Xu

Abstract

Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray da...Continue Reading

Citations

Mar 5, 2018·Mitochondrial DNA. Part B, Resources·Yong GaoLizhou Tang

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Methods Mentioned

BETA
feature extraction
feature extractions

Software Mentioned

MAF
SVM
AW

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