Authors: P. M. Granitto; F. Biasioli; C. Furlanello; F. Gasperi.
Title: Efficient feature selection for PTR-MS fingerprinting of agroindustrial products.
Resumen: 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 SVMRFE 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.
Meeting type: Congreso.
Type of job: Artículo Completo.
Production: Efficient feature selection for PTR-MS fingerprinting of agroindustrial products.
Scientific meeting: 18th International Conference on Artificial Neural Networks.
Meeting place: Prague, Czech Republic.
It's published?: Yes
Publication place: Berlin (Springer LNCS)
Meeting month: 12