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Mass spectrometry cancer data classification using wavelets and genetic algorithm
Author(s) -
Nguyen Thanh,
Nahavandi Saeid,
Creighton Douglas,
Khosravi Abbas
Publication year - 2015
Publication title -
febs letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.593
H-Index - 257
eISSN - 1873-3468
pISSN - 0014-5793
DOI - 10.1016/j.febslet.2015.11.019
Subject(s) - wavelet , pattern recognition (psychology) , robustness (evolution) , artificial intelligence , computer science , feature extraction , linear discriminant analysis , algorithm , mass spectrometry , feature (linguistics) , gabor wavelet , classifier (uml) , wavelet transform , discrete wavelet transform , chemistry , chromatography , biochemistry , gene , linguistics , philosophy
This paper introduces a hybrid feature extraction method applied to mass spectrometry (MS) data for cancer classification. Haar wavelets are employed to transform MS data into orthogonal wavelet coefficients. The most prominent discriminant wavelets are then selected by genetic algorithm (GA) to form feature sets. The combination of wavelets and GA yields highly distinct feature sets that serve as inputs to classification algorithms. Experimental results show the robustness and significant dominance of the wavelet‐GA against competitive methods. The proposed method therefore can be applied to cancer classification models that are useful as real clinical decision support systems for medical practitioners.