Cancer Classification Based on Mass Spectrometry

Authors:
Yihui Liu
Affiliations:
School of Computer Science and Information Technology, Shandong Institute of Light Industry, Jinan, Shandong, 250353, China
Venue:
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Year:
2007

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Abstract

In this paper wavelet analysis and Genetic Algorithm (GA) are performed to extract features and reduce dimensionality of mass spectrometry data. A set of wavelet features, which include detail coefficients and approximation coefficients, are extracted from mass spectrometry data. Detail coefficients are used to characterize the localized change of mass spectrometry data and approximation coefficients are used to compress mass spectrometry data, reducing the dimensionality. GA performs the further dimensionality reduction and optimizes the wavelet features. Experiments prove that this hybrid method of feature extraction is efficient way to characterize mass spectrometry data.