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2014 | OriginalPaper | Chapter

Dimensional Reduction Applied in Lung Data’s Classification

Authors : Fei Yin, Guirong Weng

Published in: Unifying Electrical Engineering and Electronics Engineering

Publisher: Springer New York

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Abstract

To improve the classification accuracy for the gene expression data, several methods (Laplacian Eigenmaps and PCA (principle component analysis)) were used to reduce the original data’s high dimension. Raw data was computed in nonlinear or linear algorithms, and classified by SVM (support vector machine). In the experiments, the classification accuracy was significantly improved after dimensional reduction. The dimensional reduction strategy can effectively improve the accuracy of classifying gene expression data.

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Metadata
Title
Dimensional Reduction Applied in Lung Data’s Classification
Authors
Fei Yin
Guirong Weng
Copyright Year
2014
Publisher
Springer New York
Book
Unifying Electrical Engineering and Electronics Engineering

Print ISBN: 978-1-4614-4980-5

Electronic ISBN: 978-1-4614-4981-2

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-1-4614-4981-2_234