A comparative investigation of modern feature selection and classification approaches for the analysis of mass spectrometry data

Gromski, PS, Xu, Y, Correa, E, Ellis, DI, Turner, ML and Goodacre, R 2014, 'A comparative investigation of modern feature selection and classification approaches for the analysis of mass spectrometry data' , Analytica Chimica Acta, 829 , pp. 1-8.

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Abstract

Many analytical approaches such as mass spectrometry generate large amounts of data (input variables) per sample analysed, and not all of these variables are important or related to the target output of interest. The selection of a smaller number of variables prior to sample classification is a widespread task in many research studies, where attempts are made to seek the lowest possible set of variables that are still able to achieve a high level of prediction accuracy; in other words, there is a need to generate the most parsimonious solution when the number of input variables is huge but the number of samples/objects are smaller. Here, we compare several different variable selection approaches in order to ascertain which of these are ideally suited to achieve this goal. All variable selection approaches were applied to the analysis of a common set of metabolomics data generated by Curie-point pyrolysis mass spectrometry (Py-MS), where the goal of the study was to classify the Gram-positive bacteria Bacillus. These approaches include stepwise forward variable selection, used for linear discriminant analysis (LDA); variable importance for projection (VIP) coefficient, employed in partial least squares-discriminant analysis (PLS-DA); support vector machines-recursive feature elimination (SVM-RFE); as well as the mean decrease in accuracy and mean decrease in Gini, provided by random forests (RF). Finally, a double cross-validation procedure was applied to minimize the consequence of overfitting. The results revealed that RF with its variable selection techniques and SVM combined with SVM-RFE as a variable selection method, displayed the best results in comparison to other approaches.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)
Journal or Publication Title: Analytica Chimica Acta
Publisher: Elsevier
ISSN: 0003-2670
Related URLs:
Depositing User: Dr Elon Correa
Date Deposited: 10 Feb 2017 14:32
Last Modified: 08 Aug 2017 12:16
URI: http://usir.salford.ac.uk/id/eprint/41369

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