AUC-RF: A New Strategy for Genomic Profiling with Random Forest [PDF]
, 2011 Objective: Genomic profiling, the use of genetic variants at multiple loci simultaneously for the prediction of disease risk, requires the selection of a set of genetic variants that best predicts disease status.
Luz Calle, M. +10 more
core +1 more source
Danger: High Power! – Exploring the Statistical Properties of a Test for Random Forest Variable Importance [PDF]
, 2008 Random forests have become a widely-used predictive model in many scientific disciplines within the past few years. Additionally, they are increasingly popular for assessing variable importance, e.g., in genetics and bioinformatics.
Zeileis, Achim +3 more
core +1 more source
Application of Sentinel-2 Satellite Data to Map Forest Cover in Southeast Sri Lanka through the Random Forest Classifier [PDF]
, 2022 Sentinel-2 satellite data has been used for forest cover monitoring for almost five years. Mapping with Sentinel data will be a cost-effective solution for Sri Lanka, where the lack of updated land cover maps with high spatial resolution is a significant
Apan, Armando +3 more
core
Conditional Variable Importance for Random Forests [PDF]
, 2008 Random forests are becoming increasingly popular in many scientific fields because they can cope with ``small n large p'' problems, complex interactions and even highly correlated predictor variables. Their variable importance measures have recently been
Augustin Thomas +14 more
core +1 more source
Random forest machine learning technique for automatic vegetation detection and modelling in LiDAR data [PDF]
, 2021 Machine learning techniques have gained a distinguished position in the automatic processing of Light Detection and Ranging (LiDAR) data area. They represent the actual research topic in the remote sensing domain.
Tarsha Kurdi, Fayez +2 more
core +1 more source
Random-effects model forest plots showing the impact of autologous BT on PCa survival after RP. [PDF]
, 2017 Random-effects model forest plots showing the impact of autologous BT on PCa survival after RP.
Su-Liang Li (3704542) +2 more
core +1 more source
Forest-Fire-Risk Prediction Based on Random Forest and Backpropagation Neural Network of Heihe Area in Heilongjiang Province, China [PDF]
, 2023 Forest fires are important factors that influence and restrict the development of forest ecosystems. In this paper, forest-fire-risk prediction was studied based on random forest (RF) and backpropagation neural network (BPNN) algorithms.
Chao Gao, Haiqing Hu, Honglei Lin
core +1 more source
Pattern Recognition Letters, 2012 In this paper, we introduce a new Random Forest (RF) induction algorithm called Dynamic Random Forest (DRF) which is based on an adaptative tree induction procedure. The main idea is to guide the tree induction so that each tree will complement as much as possible the existing trees in the ensemble.
Simon Bernard 0001 +2 more
openaire +3 more sources
The creation and analysis of next-day random forest-based high-impact weather forecasts [PDF]
, 2021 Flash floods, tornadoes, damaging winds, and large hail are costly and difficult to predict, even for state-of-the-art, high-resolution numerical weather prediction (NWP) systems. Current operational NWP ensembles have a variety of shortcomings: they are
Loken, Eric
core
Journal of Thoracic Oncology, 2011 In the article by Chen et al,1 the authors used Random Survival Forests (RSF) as part of their approach for analyzing the data. In this note, we will explain RSF in a nontechnical way; precise details of the RSF method are described in the article by Ishwaran et al.2 RSF are an adaptation of Random Forests (RF)3 designed to be used for survival data ...
openaire +2 more sources