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.
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Granitto, P.M., Biasioli, F., Furlanello, C., Gasperi, F. (2008). Efficient Feature Selection for PTR-MS Fingerprinting of Agroindustrial Products. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87559-8_5
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DOI: https://doi.org/10.1007/978-3-540-87559-8_5
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