Results 71 to 80 of about 334,720 (282)

An AUC-based Permutation Variable Importance Measure for Random Forests [PDF]

, 2012
The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs).
A Estabrooks, AL Boulesteix, AL Boulesteix, Anne-Laure Boulesteix, C Chen, C Liu, C Strobl, Carolin Strobl, F Briggs, G Batista, J Chang, J Van Hulse, J Van Hulse, K Nicodemus, KK Nicodemus, KK Nicodemus, KK Nicodemus, L Breiman, M Calle, M Cummings, M Khalilia, M Kubat, M Pepe, N Japkowicz, R Blagus, Silke Janitza, T Fawcett, T Hothorn, T Hothorn, T Khoshgoftaar, WJ Lin, Y Huang, Y Sun, Y Xie   +33 more
core   +3 more sources

Random Forest Calibration

Knowledge-Based Systems
The Random Forest (RF) classifier is often claimed to be relatively well calibrated when compared with other machine learning methods. Moreover, the existing literature suggests that traditional calibration methods, such as isotonic regression, do not substantially enhance the calibration of RF probability estimates unless supplied with extensive ...
Shaker, Mohammad Hossein, Hüllermeier, Eyke   +1 more
openaire   +3 more sources

RaMBat: Accurate identification of medulloblastoma subtypes from diverse data sources with severe batch effects

Molecular Oncology, EarlyView.
To integrate multiple transcriptomics data with severe batch effects for identifying MB subtypes, we developed a novel and accurate computational method named RaMBat, which leveraged subtype‐specific gene expression ranking information instead of absolute gene expression levels to address batch effects of diverse data sources.
Mengtao Sun, Jieqiong Wang, Shibiao Wan
wiley   +1 more source

Random Survival Forests Incorporated by the Nadaraya-Watson Regression

Информатика и автоматизация, 2022
An attention-based random survival forest (Att-RSF) is presented in the paper. The first main idea behind this model is to adapt the Nadaraya-Watson kernel regression to the random survival forest so that the regression weights or kernels can be regarded
Lev Utkin, Andrei Konstantinov
doaj   +1 more source

Analysis of a Random Forests Model [PDF]

, 2012
Random forests are a scheme proposed by Leo Breiman in the 2000's for building a predictor ensemble with a set of decision trees that grow in randomly selected subspaces of data.
Bin Yu, Gérard Biau Lsta
core   +1 more source

Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers

Molecular Oncology, EarlyView.
This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel, Peyman Zarrineh, Nathnael T. Tuffa, Jigar H. Sheth, Sumitra Mohan, Mudassar Iqbal, Sankari Nagarajan   +6 more
wiley   +1 more source

Temperature impact on the economic growth effect: method development and model performance evaluation with subnational data in China

EPJ Data Science, 2023
Temperature-economic growth relationships are computed to quantify the impact of climate change on the economy. However, model performance and differences of predictions among research complicate the use of climate econometric estimation.
Yu Song, Zhihua Pan, Fei Lun, Buju Long, Siyu Liu, Guolin Han, Jialin Wang, Na Huang, Ziyuan Zhang, Shangqian Ma, Guofeng Sun, Cong Liu   +11 more
doaj   +1 more source

ABC random forests for Bayesian parameter inference [PDF]

, 2018
This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100036). Approximate Bayesian computation (ABC) has grown into a standard methodology that manages Bayesian inference for ...
Estoup, Arnaud, Marin, Jean-Michel, Pudlo, Pierre, Raynal, Louis, Ribatet, Mathieu, Robert, Christian P.   +5 more
core   +4 more sources

Deep Learning–Assisted Differentiation of Four Peripheral Neuropathies Using Corneal Confocal Microscopy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Peripheral neuropathies contribute to patient disability but may be diagnosed late or missed altogether due to late referral, limitation of current diagnostic methods and lack of specialized testing facilities. To address this clinical gap, we developed NeuropathAI, an interpretable deep learning–based multiclass classification ...
Chaima Ben Rabah, Ioannis N. Petropoulos, Mark Stettner, Maryam Ferdousi, Uazman Alam, Nathan Efron, Ahmed Serag, Rayaz A. Malik   +7 more
wiley   +1 more source

Prediction of glycosylation sites using random forests

BMC Bioinformatics, 2008
Background Post translational modifications (PTMs) occur in the vast majority of proteins and are essential for function. Prediction of the sequence location of PTMs enhances the functional characterisation of proteins.
Hirst Jonathan D, Hamby Stephen E
doaj   +1 more source