Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products

By Pablo M. Granitto, Cesare Furlanello, Franco Biasioli, Flavia Gasperi. 2006

592 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ccdc158daa3267cbad502823b554d177f26ed7e3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Bjoern H Menze
2. B. Michael Kelm
3. Fred A. Hamprecht
4. Peter Bachert
5. Wolfgang Petrich
6. Uwe Himmelreich
7. Ralf Masuch

Citations in the corpus

• A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data (2009, methodology): “A feature selection based on the random forest classifier [14] has been found to provide multivariate feature importance scores which are relatively cheap to obtain, and which have been successfully applied to high dimensional data, arising from microarrays [15-20], time series [21], even on spectra”