A new variable importance measure for random forests with missing data

The use of classification trees for bioinformatics

(2011) Xiang Chen et al.   Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery

Maximal conditional chi-square importance in random forests

(2010) Minghui Wang et al.   BIOINFORMATICS

Permutation importance: a corrected feature importance measure

(2010) André Altmann et al.   BIOINFORMATICS

The behaviour of random forest permutation-based variable importance measures under predictor correlation

(2010) Kristin K Nicodemus et al.   BMC BIOINFORMATICS

Predicting individual tree attributes from airborne laser point clouds based on the random forests technique

(2010) Xiaowei Yu et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

Missing covariate data in medical research: To impute is better than to ignore

(2010) Kristel J.M. Janssen et al.   JOURNAL OF CLINICAL EPIDEMIOLOGY

Multiple imputation using chained equations: Issues and guidance for practice

(2010) Ian R. White et al.   STATISTICS IN MEDICINE

Dealing with Missing Predictor Values When Applying Clinical Prediction Models

(2009) K. J.M. Janssen et al.   CLINICAL CHEMISTRY

Conditional Variable Importance for Random Forests

(2008) Carolin Strobl et al.   BMC BIOINFORMATICS

Empirical characterization of random forest variable importance measures

(2007) Kellie J. Archer et al.   COMPUTATIONAL STATISTICS & DATA ANALYSIS

A framework to identify physiological responses in microarray-based gene expression studies: selection and interpretation of biologically relevant genes

(2007) Wendy Rodenburg et al.   PHYSIOLOGICAL GENOMICS