A weighted random survival forest

  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.},
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
Weighted Random Forests to Improve Arrhythmia Classification
It was proved that the proposed weighting approach improved classification accuracy for the three most challenging out of the five investigated arrhythmias comparing to the standard Random Forest model. Expand
Weighted Quantile Regression Forests for Bimodal Distribution Modeling: A Loss Given Default Case
Through the research, it is shown that weighted quantile Regression Forests outperform “single” state-of-the-art models in terms of their accuracy and the stability. Expand
Early detection of prostate gland and breast cancer risk based on routine check-up data using survival analysis trees for left-truncated and right-censored data
A novel ensemble method for risk prediction of multivariate time series data using a random forest model of survival trees for left truncated and right-censored data that can complement existing screening tests and help in detection of subjects that were missed by these tests. Expand
Using machine learning methods to predict hepatic encephalopathy in cirrhotic patients with unbalanced data
  • Hong Yang, Xinxin Li, +4 authors Yanbo Zhang
  • Computer Science, Medicine
  • Comput. Methods Programs Biomed.
  • 2021
The WRF model is more suitable for the classification of unbalanced medical data and can be used to construct a risk prediction and evaluation system for liver cirrhosis complicated with HE and the probabilistic prediction models of WRF can help clinicians identify high-risk patients with HE. Expand
Predicting the use frequency of ride-sourcing by off-campus university students through random forest and Bayesian network techniques
Two of the most broadly used machine learning techniques, Random Forest technique and Bayesian network analysis were applied to establish the relationship between ride-sourcing usage frequency and students' socio-demographic related factors, built environment considerations, and attitudes towards ride-Sourcing specific factors. Expand
First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam
Results reveal that despite slight recoveries in 2000 and 2010, the net loss of forests mainly transformed to croplands over 30 years, and the explicitly spatio-temporal VLUCDs can be benchmarks for global LULC validation, and utilized for a variety of applications in the research of environmental changes towards the Sustainable Development Goals. Expand
AIzimov: the Platform for Intellectual Diagnostics of Lung Cancer
The paper describes the practical approach of building a platform for collaboration between doctors, computer and data scientists. This study is related to a research project of creating intellectualExpand
GLAUDIA: A predicative system for glaucoma diagnosis in mass scanning
Results indicate that the proposed model can reduce FNR dramatically while maintaining a reasonable overall accuracy which makes it suitable for mass screening. Expand
A territory-wide study of arrhythmogenic right ventricular cardiomyopathy patients from Hong Kong
Clinical and electrocardiographic parameters are important for assessing prognosis in ARVC/D patients and should be used in tandem to aid risk stratification in the hospital setting and all were significantly improved by machine learning techniques. Expand
Ensemble learning updating classifier for accurate land cover assessment in tropical cloudy areas
Land use/cover information is fundamental for the sustainable management of resources. Notwithstanding the advancement of remote sensing, analysts daunt to generate sufficient-quality land use/cove...


Weighted vote for trees aggregation in Random Forest
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
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
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
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
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
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
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
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