A weighted random survival forest

@article{Utkin2019AWR,
  title={A weighted random survival forest},
  author={Lev V. Utkin and Andrei V. Konstantinov and Viacheslav S. Chukanov and Mikhail V. Kots and Mikhail A. Ryabinin and Anna A. Meldo},
  journal={Knowl. Based Syst.},
  year={2019},
  volume={177},
  pages={136-144}
}
A weighted random survival forest is presented in the paper. It can be regarded as a modification of the random forest improving its performance. The main idea underlying the proposed model is to replace the standard procedure of averaging used for estimation of the random survival forest hazard function by weighted avaraging where the weights are assigned to every tree and can be veiwed as training paremeters which are computed in an optimal way by solving a standard quadratic optimization… Expand
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References

SHOWING 1-10 OF 103 REFERENCES
Weighted vote for trees aggregation in Random Forest
TLDR
It is shown that the prediction performance of RF's can still be improved by replacing the GINI index with another index (twoing or deviance). Expand
Random survival forests
TLDR
This article introduces random survival forests, a random forests method for the analysis of right-censored survival data, and extends Breiman’s random forests (RF) method, showing it to be highly accurate and comparable to state-of-the-art methods. Expand
A random forest guided tour
The random forest algorithm, proposed by L. Breiman in 2001, has been extremely successful as a general-purpose classification and regression method. The approach, which combines several randomizedExpand
Random rotation survival forest for high dimensional censored data
TLDR
It is shown that the proposed method random rotation survival forest outperforms state-of-the-art survival ensembles such as random survival forest and popular regularized Cox models in high dimensional censored time-to-event data analysis. Expand
Survival forest with partial least squares for high dimensional censored data
Abstract Random forest and partial least squares have proved wide applicability in numerous contexts. However, the combination of these versatile tools has seldom been studied. Inspired by aExpand
Random forests for survival analysis using maximally selected rank statistics
TLDR
The new method performs better than random survival forests if informative dichotomous variables are combined with uninformative variables with more categories and better than conditional inference forests if non-linear covariate effects are included. Expand
Random Forests
  • L. Breiman
  • Mathematics, Computer Science
  • Machine Learning
  • 2004
TLDR
Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression. Expand
A Bayesian Approach to Sparse Cox Regression in High-Dimentional Survival Analysis
TLDR
A new Bayesian framework for feature selection in high-dimensional Cox regression problems is suggested and a strong probabilistic statement of the shrinkage criterion for features selection is given. Expand
Dynamic Random Forests
TLDR
A new Random Forest induction algorithm called Dynamic Random Forest (DRF) which is based on an adaptative tree induction procedure which shows a significant improvement in terms of accuracy compared to the standard static RF induction algorithm. Expand
Random Survival Forests.
  • Jeremy MG Taylor
  • Medicine
  • Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer
  • 2011
TLDR
RSF is an adaptation of Random Forests designed to be used for survival data, and it has been shown that injecting some controlled variation or randomness into the construction of each of the separate trees can improve prediction performance. Expand
...
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3
4
5
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