TY - JOUR AU - Menze, Bjoern H. AU - Kelm, B. Michael AU - Masuch, Ralf AU - Himmelreich, Uwe AU - Bachert, Peter AU - Petrich, Wolfgang AU - Hamprecht, Fred A. PY - 2009 DA - 2009/07/10 TI - A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data JO - BMC Bioinformatics SP - 213 VL - 10 IS - 1 AB - 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, on the other hand, allows for an explicit feature elimination, but may not be optimally adapted to spectral data due to the topology of its constituent classification trees which are based on orthogonal splits in feature space. SN - 1471-2105 UR - https://doi.org/10.1186/1471-2105-10-213 DO - 10.1186/1471-2105-10-213 ID - Menze2009 ER -