A weighted random survival forest

Utkin, Lev V.; Konstantinov, Alexander A.; Chukanov, Viacheslav S.; Kots, Mikhail V.; Ryabinin, M. A.; Meldo, Anna A.

doi:10.1016/j.knosys.2019.04.015

Cited by 35 publications

(16 citation statements)

References 80 publications

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“…Numerous algorithms have been developed to improve LULC classification, e.g. Spatial Temporal Adaptive Algorithm 8 , Automatic Land Cover Classification Method 9 , and Apply Change-vector Analysis in Posterior Probability Space 10 . Together with the development of these complex algorithms, special projects have been designed for large-scale land cover assessment.…”

Section: Introductionmentioning

confidence: 99%

First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam

Phan

Trung

Truong

et al. 2021

Sci Rep

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Extensive studies have highlighted a need for frequently consistent land cover information for interdisciplinary studies. This paper proposes a comprehensive framework for the automatic production of the first Vietnam-wide annual land use/land cover (LULC) data sets (VLUCDs) from 1990 to 2020, using available remotely sensed and inventory data. Classification accuracies ranged from 85.7 ± 1.3 to 92.0 ± 1.2% with the primary dominant LULC and 77.6 ± 1.2% to 84.7 ± 1.1% with the secondary dominant LULC. This confirmed the potential of the proposed framework for systematically long-term monitoring LULC in Vietnam. Results reveal that despite slight recoveries in 2000 and 2010, the net loss of forests (19,940 km2) mainly transformed to croplands over 30 years. Meanwhile, productive croplands were converted to urban areas, which increased approximately ten times. A threefold increase in aquaculture was a major driver of the wetland loss (1914 km2). The spatial–temporal changes varied, but the most dynamic regions were the western north, the southern centre, and the south. These findings can provide evidence-based information on formulating and implementing coherent land management policies. 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.

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Section: Introductionmentioning

confidence: 99%

First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam

Phan

Trung

Truong

et al. 2021

Sci Rep

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“…Random survival forests (RSF), introduced by Ishwaran [35] and combined the concepts of Breiman’s random forest [36,37], survival trees and the log-rank test as the splitting criterion. An extension of RSF is the utilization of conditional inference trees, which use hypothesis testing to select the splitting covariates and also as a stopping criterion [38], among other improvements that were examined [39,40].…”

Section: Background and Significancementioning

confidence: 99%

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

Coster

Fischer

Shenhar‐Tsarfaty

et al. 2021

Preprint

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ObjectiveTo predict breast cancer (BC) and prostate gland cancer (PGC) risk among healthy individuals by analyzing routine laboratory measurements, vital signs and age.Materials and MethodsWe analyzed electronic medical records of 20,317 healthy individuals who underwent routine checkups, encompassing more than 600 parameters per visit, and identified those who later developed cancer. We developed 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.ResultsUsing cross-validation, our method predicted future PGC and BC 6 months before diagnosis, achieving an area under the ROC curve of 0.62±0.05 and 0.6±0.03 respectively, better than standard random forest, Cox-regression model and a single survival tree. Our method can complement existing screening tests such as clinical breast examination and mammography for BC, and help in detection of subjects that were missed by these tests.DiscussionComputational analysis of results of routine checkups of healthy individuals can improve the detection of those at risk of cancer development.ConclusionOur method may assist in early detection of breast and prostate gland cancer.

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“…The results show that recency-weighted ensembles of Random Forests produce superior results, in terms of both profitability and prediction accuracy, compared to other ensemble techniques. Utkin et al [24] proposed weighted Random Survival Forest which 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 averaging.…”

Section: Literature Reviewmentioning

confidence: 99%

Weighted Random Forests to Improve Arrhythmia Classification

et al. 2020

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Construction of an ensemble model is a process of combining many diverse base predictive learners. It arises questions of how to weight each model and how to tune the parameters of the weighting process. The most straightforward approach is simply to average the base models. However, numerous studies have shown that a weighted ensemble can provide superior prediction results to a simple average of models. The main goals of this article are to propose a new weighting algorithm applicable for each tree in the Random Forest model and the comprehensive examination of the optimal parameter tuning. Importantly, the approach is motivated by its flexibility, good performance, stability, and resistance to overfitting. The proposed scheme is examined and evaluated on the Physionet/Computing in Cardiology Challenge 2015 data set. It consists of signals (electrocardiograms and pulsatory waveforms) from intensive care patients which triggered an alarm for five cardiac arrhythmia types (Asystole, Bradycardia, Tachycardia, Ventricular Tachycardia, and Ventricular Fultter/Fibrillation). The classification problem regards whether the alarm should or should not have been generated. 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.

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A weighted random survival forest

Cited by 35 publications

References 80 publications

First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam

First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam

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

Weighted Random Forests to Improve Arrhythmia Classification

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