Mass spectrometry cancer data classification using wavelets and genetic algorithm
Nguyen, Thanh, Nahavandi, Saeid, Creighton, Douglas and Khosravi, Abbas 2015, Mass spectrometry cancer data classification using wavelets and genetic algorithm, FEBS letters, vol. 589, no. 24 part b, pp. 3879-3886, doi: 10.1016/j.febslet.2015.11.019.
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Mass spectrometry cancer data classification using wavelets and genetic algorithm
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.
080108 Neural, Evolutionary and Fuzzy Computation 080109 Pattern Recognition and Data Mining 0601 Biochemistry And Cell Biology 0304 Medicinal And Biomolecular Chemistry
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences
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