A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data
Background Regularized regression methods such as principal component or partial least squares regression perform well in learning tasks on high dimensional spectral data, but cannot explicitly eliminate irrelevant features. The random forest classifier with its associated Gini feature importance...
| Main Authors: | , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
BioMed Central Ltd.
2010
|
| Online Access: | http://hdl.handle.net/1721.1/52355 |