Machine Learning
Is bagging effective in the classification of small-sample genomic and proteomic data?
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
Ensemble Approach for the Classification of Imbalanced Data
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Small-sample error estimation for bagged classification rules
EURASIP Journal on Advances in Signal Processing - Special issue on genomic signal processing
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There has been much progresses recently about the identification of diagnostic proteomic signatures for different human cancers using surface-enhanced laser desorption ionization time-of-flight (SELDITOF) mass spectrometry. To identify proteomic patterns in serum to discriminate cancer patients from normal individuals, many classification methods have been experimented, often with successful results. Most of these earlier studies, however, are based on the direct application of original mass spectra, together with dimension reduction methods like PCA or feature selection methods like T-tests. Because only the peaks of MS data correspond to potential biomarkers, it is important to study classification methods using the detected peaks. This paper investigates ovarian cancer identification from the detected MS peaks by applying Bagging Support Vector Machine as a special strategy of bootstrap aggregating (Bagging). In bagging SVM, each individual SVM is trained independently, using randomly chosen training samples via a bootstrap technique. The trained individual SVMs are aggregated to make a collective decision in an appropriate way, for example, the majority voting. Bagged SVM demonstrated a 94% accuracy with 95% sensitivity and 92% specificity respectively by using the detected peaks. The efficiency can be further improved by applying PCA to reduce the dimension.