Survival ensembles.

By Torsten Hothorn, Peter Buhlmann, Sandrine Dudoit, Annette M. Molinaro, M. J. van der Laan. Biostatistics, 2006

638 citations recorded by the source corpus.

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Citations in the corpus

Phonetics and politeness: Perceiving Korean honorific and non-honorific speech through phonetic cues (2014, methodology): “368 In this section, we explore what cues were relevant to listener’s choices, using random forests8 (Breiman, 2001), 369 implemented by the partyR package (Hothorn et al., 2006; Strobl et al., 2007, 2008).”
Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy (2018, methodology): “To this purpose, we consider the feedback classifier Ft , as this is the one mainly affected by the SSB due to alert–feedback interaction, and use a weight-sensitive implementation of the RFs based on conditional inference trees [40].”
Machine Learning for Survival Analysis: A Survey (2017, background): “X, Article 1, Publication date: August 2017. dient descent algorithm [Hothorn et al. 2006; B¨uhlmann and Hothorn 2007].”
Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics (2012, background): “A summary Name RF only MT System Code ALGLIB [3] no no Win/Unix C++ cforest function in R package party [37] no yes all C++/S FastRandomForest [71] yes yes all ∗ Java Orange [22] no no Win/Unix/Mac C++/Python PARF - Parallel RF Algorithm [75] yes yes all ∗ F90 Random forest [9] yes no all ∗ F77…”
Boosting Algorithms: Regularization, Prediction and Model Fitting. Rejoinder. (2007, methodology): “Key words and phrases: Generalized linear models, generalized additive models, gradient boosting, survival analysis, variable selection, software.”
Revisiting acoustic correlates of pharyngealization in Jordanian and Moroccan Arabic: Implications for formal representations (2017, methodology): “Instead of using the original implementation of Random Forests available in the R package randomForest (Liaw & Wiener, 2002), we used Random Forests grown from Conditional Inference Trees as implemented in the package party (Hothorn et al., 2006; Strobl et al., 2008, 2007).”
High-Dimensional Variable Selection for Survival Data (2010, methodology): “Note that RSF differs from the RF approach of Hothorn et al. (2006) and the conditional inference method described by Hothorn, Hornik, and Zeileis (2006) (see Ishwaran et al. 2008 for a discussion of differences among the various methods).”
Random Forests for Genomic Data Analysis (2012, methodology): “We note that the R-package party [10] also provides a uni ﬁ ed forest treatment, although the approach makes use of conditional trees and is different than Breiman's RF.”
scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn (2020, background): “Today, many successful ideas from machine learning have been adapted for time-to-event analysis, such as gradient boosting (Ridgeway, 1999; Hothorn et al., 2006), random forests (Ishwaran et al., 2008), and support vector machines (Van Belle et al., 2007; Evers and Messow, 2008; Pölsterl et al.,…”
Machine Learning for Survival Analysis (2019, methodology): “Ishwaran et al. (2011) also provides an effective way to apply RSF for high-dimensional survival analysis problems by regularizing forests.”
A review of survival trees (2011, methodology): “Another direction was to study the use of ensemble methods with survival trees (Ishwaran et al., 2004; Hothorn et al., 2004, 2006; Krȩtowska, 2006, 2010; Ishwaran et al., 2008).”
Random survival forests (2008, methodology): “One of these, node-positive breast cancer data studied in Hothorn et al. (2006), was used to create three additional datasets.”