Efficient Feature Selection for PTR-MS Fingerprinting of Agroindustrial Products

Abstract

We recently introduced the Random Forest - Recursive Feature Elimination (RF-RFE) algorithm for feature selection. In this paper we apply it to the identification of relevant features in the spectra (fingerprints) produced by Proton Transfer Reaction - Mass Spectrometry (PTR-MS) analysis of four agro-industrial products (two datasets with cultivars of Berries and other two with typical cheeses, all from North Italy). The method is compared with the more traditional Support Vector Machine - Recursive Feature Elimination (SVM-RFE), extended to allow multiclass problems. Using replicated experiments we estimate unbiased generalization errors for both methods. We analyze the stability of the two methods and find that RF-RFE is more stable than SVM-RFE in selecting small subsets of features. Our results also show that RF-RFE outperforms SVM-RFE on the task of finding small subsets of features with high discrimination levels on PTR-MS datasets.KeywordsSupport Vector MachineFeature SelectionFeature Selection MethodRecursive Feature EliminationFeature Selection ProcessThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.