Publication Date:
2015-12-01
Description:
Publication date: Available online 30 November 2015 Source: FEBS Letters Author(s): Thanh Nguyen, Saeid Nahavandi, Douglas Creighton, Abbas Khosravi 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.
Print ISSN:
0014-5793
Electronic ISSN:
1873-3468
Topics:
Biology
,
Chemistry and Pharmacology
,
Physics
