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Journal articles on the topic 'Random forest'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Mantas, Carlos J., Javier G. Castellano, Serafín Moral-García, and Joaquín Abellán. "A comparison of random forest based algorithms: random credal random forest versus oblique random forest." Soft Computing 23, no. 21 (2018): 10739–54. http://dx.doi.org/10.1007/s00500-018-3628-5.

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Rigatti, Steven J. "Random Forest." Journal of Insurance Medicine 47, no. 1 (2017): 31–39. http://dx.doi.org/10.17849/insm-47-01-31-39.1.

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For the task of analyzing survival data to derive risk factors associated with mortality, physicians, researchers, and biostatisticians have typically relied on certain types of regression techniques, most notably the Cox model. With the advent of more widely distributed computing power, methods which require more complex mathematics have become increasingly common. Particularly in this era of “big data” and machine learning, survival analysis has become methodologically broader. This paper aims to explore one technique known as Random Forest. The Random Forest technique is a regression tree t

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3

Yamaoka, Keisuke. "Random Forest." Journal of The Institute of Image Information and Television Engineers 66, no. 7 (2012): 573–75. http://dx.doi.org/10.3169/itej.66.573.

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x, Adeen, and Preeti Sondhi. "Random Forest Based Heart Disease Prediction." International Journal of Science and Research (IJSR) 10, no. 2 (2021): 1669–72. https://doi.org/10.21275/sr21225214148.

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MISHINA, Yohei, Ryuei MURATA, Yuji YAMAUCHI, Takayoshi YAMASHITA, and Hironobu FUJIYOSHI. "Boosted Random Forest." IEICE Transactions on Information and Systems E98.D, no. 9 (2015): 1630–36. http://dx.doi.org/10.1587/transinf.2014opp0004.

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6

Han, Sunwoo, Hyunjoong Kim, and Yung-Seop Lee. "Double random forest." Machine Learning 109, no. 8 (2020): 1569–86. http://dx.doi.org/10.1007/s10994-020-05889-1.

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Cho, Yunsub, Soowoong Jeong, and Sangkeun Lee. "Positive Random Forest based Robust Object Tracking." Journal of the Institute of Electronics and Information Engineers 52, no. 6 (2015): 107–16. http://dx.doi.org/10.5573/ieie.2015.52.6.107.

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Wagle, Aumkar. "Random Forest Classifier to Predict Financial Data." International Journal of Science and Research (IJSR) 13, no. 4 (2024): 1932–43. http://dx.doi.org/10.21275/sr24418155701.

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Shahwan, younis ali, and Sedqi Kareem Omar. "Forest Fire Prediction using Random Forest." Engineering and Technology Journal 10, no. 05 (2025): 5159–64. https://doi.org/10.5281/zenodo.15489145.

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Forest fires pose significant ecological, economic, and human threats, demanding accurate and timely prediction systems for effective mitigation. This study proposes a machine learning-based approach using the Random Forest (RF) algorithm to predict the likelihood of forest fire occurrences based on environmental and meteorological variables. Utilizing the UCI Forest Fires dataset, the model classifies fire risk by analyzing features such as temperature, relative humidity, wind speed, drought code, and the initial spread index. The dataset underwent preprocessing steps including normalization,

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Salman, Hasan Ahmed, Ali Kalakech, and Amani Steiti. "Random Forest Algorithm Overview." Babylonian Journal of Machine Learning 2024 (June 8, 2024): 69–79. http://dx.doi.org/10.58496/bjml/2024/007.

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A random forest is a machine learning model utilized in classification and forecasting. To train machine learning algorithms and artificial intelligence models, it is crucial to have a substantial amount of high-quality data for effective data collecting. System performance data is essential for refining algorithms, enhancing the efficiency of software and hardware, evaluating user be-havior, enabling pattern identification, decision-making, predictive modeling, and problem-solving, ultimately resulting in improved effectiveness and accuracy. The integration of diverse data collecting and proc

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LIU, Zhi, Zhaocai SUN, and Hongjun WANG. "Specific Random Trees for Random Forest." IEICE Transactions on Information and Systems E96.D, no. 3 (2013): 739–41. http://dx.doi.org/10.1587/transinf.e96.d.739.

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Sulistiyono, N., C. P. Tarigan, A. F. Daulay, S. A. Hudjimartsu, and Y. U. Putri. "Identification of mangrove forests using Sentinel-1 Synthetic Aperture Radar (SAR) satellite imagery in Medan Belawan District, Medan City." IOP Conference Series: Earth and Environmental Science 1352, no. 1 (2024): 012051. http://dx.doi.org/10.1088/1755-1315/1352/1/012051.

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Abstract Mangroves are a forest ecosystem tolerant to salt (halophytic) and influenced by sea tides. Mangrove forests are producers of ecosystem services that all living things need. The use of remote sensing can be used to identify mangrove forests in large areas in a short time. Sentinel-1 is a satellite product widely used to monitor land cover, including mangrove forests. The research objective was to identify mangrove forests in the Medan Belawan District using Sentinel-1 satellite imagery. Land cover classification in digital images was done using supervised classification with the maxim

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Zhang, Gongqiao, and Gangying Hui. "Random Trees Are the Cornerstones of Natural Forests." Forests 12, no. 8 (2021): 1046. http://dx.doi.org/10.3390/f12081046.

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Natural forests serve as the main component of the forest ecosystem. An in-depth interpretation of tree composition and structure of forest community is of great significance for natural forest conservation, monitoring, management, and near-natural silviculture of plantation forest. In this study, we explored the importance of key tree groups—random trees—in natural communities, compared the similarity between the random trees and the communities. This research studies six stem-mapped permanent plots (100 × 100 m2) of the typical natural forests in three different geographic regions of China.

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Roy, Marie-Hélène, and Denis Larocque. "Prediction intervals with random forests." Statistical Methods in Medical Research 29, no. 1 (2019): 205–29. http://dx.doi.org/10.1177/0962280219829885.

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The classical and most commonly used approach to building prediction intervals is the parametric approach. However, its main drawback is that its validity and performance highly depend on the assumed functional link between the covariates and the response. This research investigates new methods that improve the performance of prediction intervals with random forests. Two aspects are explored: The method used to build the forest and the method used to build the prediction interval. Four methods to build the forest are investigated, three from the classification and regression tree (CART) paradi

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Puerta, Ronald, and José Iannacone. "Analysis of forest cover in Parque Nacional Tingo María (Peru) using the random forest algorithm." Scientia Agropecuaria 14, no. 3 (2023): 291–300. http://dx.doi.org/10.17268/sci.agropecu.2023.025.

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The establishment of natural protected areas is one of the most effective strategies to conserve forests and their biodiversity; however, the uncontrolled advance of deforestation resulting from the change of use to expand the agricultural frontier has become a threat to these intangible areas. This research aimed to analyze the dynamics of forest cover in Parque Nacional Tingo María (PNTM) and its buffer zone (ZA) located in the high jungle of the Huánuco region of Peru. The main input was Sentinel-2 images that were classified using the Random Forest algorithm. As a result, coverage maps wer

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Mathew, Dr Tina Elizabeth. "An Improvised Random Forest Model for Breast Cancer Classification." NeuroQuantology 20, no. 5 (2022): 713–22. http://dx.doi.org/10.14704/nq.2022.20.5.nq22227.

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Breast Cancer is considered as the most common cancer in females with high incidence rate. The evolution of modern facilities has helped in reducing the mortality rate, yet the incidence is still the highest among all cancers affecting women. Early diagnosis is a predominant factor for survival. Hence techniques to assist the current modalities are essential. Machine learning techniques have been used so as to produce better prediction and classification models which will aid in better and earlier disease diagnosis and classification. Random Forest is a supervised machine learning classifier t

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Yu, Tianyu, Cuiwei Liu, Zhuo Yan, and Xiangbin Shi. "A Multi-Task Framework for Action Prediction." Information 11, no. 3 (2020): 158. http://dx.doi.org/10.3390/info11030158.

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Predicting the categories of actions in partially observed videos is a challenging task in the computer vision field. The temporal progress of an ongoing action is of great importance for action prediction, since actions can present different characteristics at different temporal stages. To this end, we propose a novel multi-task deep forest framework, which treats temporal progress analysis as a relevant task to action prediction and takes advantage of observation ratio labels of incomplete videos during training. The proposed multi-task deep forest is a cascade structure of random forests an

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Dai, Hongsheng. "Perfect sampling methods for random forests." Advances in Applied Probability 40, no. 3 (2008): 897–917. http://dx.doi.org/10.1239/aap/1222868191.

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A weighted graphGis a pair (V, ℰ) containing vertex setVand edge set ℰ, where each edgee∈ ℰ is associated with a weightWe. A subgraph ofGis a forest if it has no cycles. All forests on the graphGform a probability space, where the probability of each forest is proportional to the product of the weights of its edges. This paper aims to simulate forests exactly from the target distribution. Methods based on coupling from the past (CFTP) and rejection sampling are presented. Comparisons of these methods are given theoretically and via simulation.

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Koptev, Sergey V., and Hasan Alabdullahalhasno. "Applying the Random Forest Algorithm to Analyze the Dynamics of Taiga-Tundra Forest Ecosystems." Lesnoy Zhurnal (Forestry Journal), no. 2 (April 5, 2025): 210–18. https://doi.org/10.37482/0536-1036-2025-2-210-218.

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The article presents the results of studying the dynamics of taiga-tundra forest ecosystems in the Arkhangelsk Region based on the Earth remote sensing data and the application of image classification using the Random Forest algorithm. The change in the proportion of forested, non-forested and unforested areas in the study area is noted in the forest register. The results of the study show a significant increase in forest area between 2016 and 2023 (by 10.28 %) due to a reduction in non-forested areas and unstocked forest lands. This dynamics is due to the processes of successful natural resto

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Dai, Hongsheng. "Perfect sampling methods for random forests." Advances in Applied Probability 40, no. 03 (2008): 897–917. http://dx.doi.org/10.1017/s0001867800002846.

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A weighted graph G is a pair (V, ℰ) containing vertex set V and edge set ℰ, where each edge e ∈ ℰ is associated with a weight We . A subgraph of G is a forest if it has no cycles. All forests on the graph G form a probability space, where the probability of each forest is proportional to the product of the weights of its edges. This paper aims to simulate forests exactly from the target distribution. Methods based on coupling from the past (CFTP) and rejection sampling are presented. Comparisons of these methods are given theoretically and via simulation.

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Dumarevskaya, Liubov, and Jason R. Parent. "Modeling Spongy Moth Forest Mortality in Rhode Island Temperate Deciduous Forest." Forests 16, no. 1 (2025): 93. https://doi.org/10.3390/f16010093.

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Invasive pests cause major ecological and economic damages to forests around the world including reduced carbon sequestration and biodiversity and loss of forest revenue. In this study, we used Random Forest to model forest mortality resulting from a 2015–2017 Spongy moth outbreak in the temperate deciduous forests of Rhode Island (northeastern U.S.). Mortality was modeled with a 100 m spatial resolution based on Landsat-derived defoliation maps and geospatial data representing soil characteristics, drought condition, and forest characteristics as well as proximity to coast, development, and w

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Sekulić, Aleksandar, Milan Kilibarda, Gerard B. M. Heuvelink, Mladen Nikolić, and Branislav Bajat. "Random Forest Spatial Interpolation." Remote Sensing 12, no. 10 (2020): 1687. http://dx.doi.org/10.3390/rs12101687.

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For many decades, kriging and deterministic interpolation techniques, such as inverse distance weighting and nearest neighbour interpolation, have been the most popular spatial interpolation techniques. Kriging with external drift and regression kriging have become basic techniques that benefit both from spatial autocorrelation and covariate information. More recently, machine learning techniques, such as random forest and gradient boosting, have become increasingly popular and are now often used for spatial interpolation. Some attempts have been made to explicitly take the spatial component i

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23

Goel, Eesha, and Er Abhilasha. "Random Forest: A Review." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 1 (2017): 251–57. http://dx.doi.org/10.23956/ijarcsse/v7i1/01113.

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Zhang, Haozhe, Joshua Zimmerman, Dan Nettleton, and Daniel J. Nordman. "Random Forest Prediction Intervals." American Statistician 74, no. 4 (2019): 392–406. http://dx.doi.org/10.1080/00031305.2019.1585288.

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Paul, Angshuman, and Dipti Prasad Mukherjee. "Reinforced quasi-random forest." Pattern Recognition 94 (October 2019): 13–24. http://dx.doi.org/10.1016/j.patcog.2019.05.013.

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Katuwal, Rakesh, P. N. Suganthan, and Le Zhang. "Heterogeneous oblique random forest." Pattern Recognition 99 (March 2020): 107078. http://dx.doi.org/10.1016/j.patcog.2019.107078.

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27

Bonissone, Piero, José M. Cadenas, M. Carmen Garrido, and R. Andrés Díaz-Valladares. "A fuzzy random forest." International Journal of Approximate Reasoning 51, no. 7 (2010): 729–47. http://dx.doi.org/10.1016/j.ijar.2010.02.003.

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Akay, Ebru Çaglayan, Kadriye Hilal Topal, Saban Kizilarslan, and Hoseng Bulbul. "Forecasting of Turkish housing price index: ARIMA, random forest, ARIMA-random forest." Pressacademia 10, no. 10 (2019): 7–11. http://dx.doi.org/10.17261/pressacademia.2019.1134.

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Shalini, G., V. Mounika, U. P. Sannidhi, Y. N. Swathi, Deepak N. R. Dr., and B. Omprakash. "Crime Prediction Using Random Forest Model." Journal of Advances in Computational Intelligence Theory 7, no. 2 (2025): 31–38. https://doi.org/10.5281/zenodo.15331869.

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<em>The most serious issue in present scenario is crime, which disrupts the people’s lives. The crime will not occur not only in certain time of human lives but it will occur at any time in person’s life, whether they are travelling home from work or going on trip etc. In terms of public safety, crime prediction is required. We can predict the crimes using Random Forest Model. This model will help the authorized officers do their work efficiently as well as effectively so that they can avoid the crime before it occurs. Depending on the quality of the data, the crime prediction accu

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Kumano, So, and Tatsuya Akutsu. "Comparison of the Representational Power of Random Forests, Binary Decision Diagrams, and Neural Networks." Neural Computation 34, no. 4 (2022): 1019–44. http://dx.doi.org/10.1162/neco_a_01486.

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Abstract In this letter, we compare the representational power of random forests, binary decision diagrams (BDDs), and neural networks in terms of the number of nodes. We assume that an axis-aligned function on a single variable is assigned to each edge in random forests and BDDs, and the activation functions of neural networks are sigmoid, rectified linear unit, or similar functions. Based on existing studies, we show that for any random forest, there exists an equivalent depth-3 neural network with a linear number of nodes. We also show that for any BDD with balanced width, there exists an e

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Novikova, Tatyana, Svetlana Evdokimova, and Gotsui Wu. "Development of a quantitative investment algorithm based on Random Forest." Modeling of systems and processes 15, no. 4 (2022): 53–60. http://dx.doi.org/10.12737/2219-0767-2022-15-4-53-60.

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In modern research of the stock market, specialists and scientists are improving algorithms and models, combining them with each other, with strategies and market conditions for stock selection. This paper presents an overview of stock selection models for quantitative investment, which was the basis for the proposed procedure and algorithm of quantitative investment, which allow modeling the investment process. The developed algorithm is based on the CART decision tree and Random Forest, which includes the bagging algorithm. The bagging algorithm divides the training set into several new trai

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Xin Sui, Xin Sui, Hailong Zhao Xin Sui, Honghua Xu Hailong Zhao, Xiaolong Song Honghua Xu, and Dan Liu Xiaolong Song. "Load Forecasting Based on Optimized Random Forest Algorithm in Cloud Environment." 電腦學刊 35, no. 3 (2024): 013–26. http://dx.doi.org/10.53106/199115992024063503002.

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<p>To solve the problem of unbalanced resource load in cloud data center, a resource load forecasting method which is based on random forest model from the perspective of resource load forecasting is proposed in the paper. This method combines genetic algorithm with random forest algorithm to solve the problem that random forest algorithm can not determine the combination of parameter in order to obtain the optimum forecasting effect. The results of experiment show that compared with the super parametric method of random forest model, which is optimized by random search, the one optimize

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Sheridan, Robert P. "Using Random Forest To Model the Domain Applicability of Another Random Forest Model." Journal of Chemical Information and Modeling 53, no. 11 (2013): 2837–50. http://dx.doi.org/10.1021/ci400482e.

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Ibrahim, Muhammad. "Evolution of Random Forest from Decision Tree and Bagging: A Bias-Variance Perspective." Dhaka University Journal of Applied Science and Engineering 7, no. 1 (2023): 66–71. http://dx.doi.org/10.3329/dujase.v7i1.62888.

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The ensemble methods are one of the most heavily used techniques in machine learning. The random forest arguably spearheads this army of learners. Being sprung from the decision tree in the late 90s, the benefits of a random forest have rightfully attracted practitioners to widely and successfully apply this powerful yet simple-to-understand technique to numerous applications. In this study we explain the evolution of a random forest from a decision tree in the context of bias and variance of learning theory. While doing so, we focus on the interplay between the correlation and generalization

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Riyan, Bayu Satriya, and Kusnawi Kusnawi. "Random Search Optimization Using Random Forest Algorithm For Liver Disease Prediction." SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan 2, no. 3 (2025): 127–38. https://doi.org/10.5281/zenodo.15468679.

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The liver is a vital human organ with complex and diverse functions. One of the diseases that affect the liver is hepatitis or liver disease. Early detection is crucial to enable more effective intervention and slow the progression of the disease. However, diagnosing liver disease often faces challenges, especially in detecting the early stages of the disease from complex and diverse medical data. This study aims to optimize the <em>Random Forest </em>algorithm using <em>the Random Search</em> method for liver disease detection. <em>The Random Forest </em>algorith

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Isidoros, Iakovidis, and Nicola Arcozzi. "Improved convergence rates for some kernel random forest algorithms." Mathematics in Engineering 6, no. 2 (2024): 305–38. http://dx.doi.org/10.3934/mine.2024013.

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<abstract><p>Random forests are notable learning algorithms introduced by Breiman in 2001. They are widely used for classification and regression tasks and their mathematical properties are under ongoing research. We consider a specific class of random forest algorithms related to kernel methods, the so-called Kernel Random Forests (KeRF). In particular, we investigate thoroughly two explicit algorithms, designed independently of the data set, the centered KeRF and the uniform KeRF. In the present article, we provide an improvement in the rate of convergence for both algorithms and

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Mascaro, Joseph, Gregory P. Asner, David E. Knapp, et al. "A Tale of Two “Forests”: Random Forest Machine Learning Aids Tropical Forest Carbon Mapping." PLoS ONE 9, no. 1 (2014): e85993. http://dx.doi.org/10.1371/journal.pone.0085993.

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Rohman, Muhammad Ghofar, Zubaile Abdullah, Shahreen Kasim, and Rasyidah. "Hybrid Logistic Regression Random Forest on Predicting Student Performance." JOIV : International Journal on Informatics Visualization 9, no. 2 (2025): 852. https://doi.org/10.62527/joiv.9.2.3972.

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The research aims to investigate the effects of unbalanced data on machine learning, overcome imbalanced data using SMOTE oversampling, and improve machine learning performance using hyperparameter tuning. This study proposed a model that combines logistic regression and random forests as a hybrid logistic regression, random forest, and random search SV that uses SMOTE oversampling and hyperparameter tuning. The result of this study showed that the prediction model using the hybrid logistic regression, random forest, and random search SV that we proposed produces more effective performance tha

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Łuczak, Tomasz, and Boris Pittel. "Components of Random Forests." Combinatorics, Probability and Computing 1, no. 1 (1992): 35–52. http://dx.doi.org/10.1017/s0963548300000067.

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A forest ℱ(n, M) chosen uniformly from the family of all labelled unrooted forests with n vertices and M edges is studied. We show that, like the Érdős-Rényi random graph G(n, M), the random forest exhibits three modes of asymptotic behaviour: subcritical, nearcritical and supercritical, with the phase transition at the point M = n/2. For each of the phases, we determine the limit distribution of the size of the k-th largest component of ℱ(n, M). The similarity to the random graph is far from being complete. For instance, in the supercritical phase, the giant tree in ℱ(n, M) grows roughly two

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Vacek, Zdeněk, Stanislav Vacek, Lukáš Bílek, et al. "Impact of applied silvicultural systems on spatial pattern of hornbeam-oak forests." Central European Forestry Journal 64, no. 1 (2018): 33–45. http://dx.doi.org/10.1515/forj-2017-0031.

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AbstractThe spatial pattern of forest closely affects tree competition that drives the most of processes in forest ecosystems. Therefore, we focused on evaluation of the horizontal structure of high forest, coppice with standards and low forest in hornbeam-oak forests in the Protected Landscape Area Český kras (Czech Republic). The horizontal structure of tree layer individuals with crown projection centroids and natural regeneration was analysed for durmast oak (Quercus petraea(Matt.) Liebl.), European hornbeam (Carpinus betulusL.) and small-leaved linden (Tilia cordataMill.) stands. Horizont

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Huang, Tianbao, Guanglong Ou, Yong Wu, et al. "Estimating the Aboveground Biomass of Various Forest Types with High Heterogeneity at the Provincial Scale Based on Multi-Source Data." Remote Sensing 15, no. 14 (2023): 3550. http://dx.doi.org/10.3390/rs15143550.

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It is important to improve the accuracy of models estimating aboveground biomass (AGB) in large areas with complex geography and high forest heterogeneity. In this study, k-nearest neighbors (k-NN), gradient boosting machine (GBM), random forest (RF), quantile random forest (QRF), regularized random forest (RRF), and Bayesian regularization neural network (BRNN) machine learning algorithms were constructed to estimate the AGB of four forest types based on environmental factors and the variables selected by the Boruta algorithm in Yunnan Province and using integrated Landsat 8 OLI and Sentinel

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Kwon, Hyeokjun, and Jonghan Sea. "Characteristics of Sexual Homicide: Based on Random Forest Analysis." KOREAN CRIMINOLOGICAL REVIEW 33, no. 1 (2022): 165–92. http://dx.doi.org/10.36889/kcr.2022.3.31.1.165.

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Khadar Nawas, K., Manish Kumar Barik, and A. Nayeemulla Khan. "Speaker Recognition using Random Forest." ITM Web of Conferences 37 (2021): 01022. http://dx.doi.org/10.1051/itmconf/20213701022.

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Speaker identification has become a mainstream technology in the field of machine learning that involves determining the identity of a speaker from his/her speech sample. A person’s speech note contains many features that can be used to discriminate his/her identity. A model that can identify a speaker has wide applications such as biometric authentication, security, forensics and human-machine interaction. This paper implements a speaker identification system based on Random Forest as a classifier to identify the various speakers using MFCC and RPS as feature extraction techniques. The output

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Zhao, Zi Ming, Cui Hua Li, Hua Shi, and Quan Zou. "Material Classification Using Random Forest." Advanced Materials Research 301-303 (July 2011): 73–79. http://dx.doi.org/10.4028/www.scientific.net/amr.301-303.73.

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Random forest has demonstrated excellent performance to deal with many problems of computer vision, such as image classification and keypoint recognition. This paper proposes an approach to classify materials, which combines random forest with MR8 filter bank. Firstly, we employ MR8 filter bank to filter the texture image. These filter responses are taken as texture feature. Secondly, Random forest grows on sub-window patches which are randomly extracted from these filter responses, then we use this trained forest to classify a given image (under unknown viewpoint and illumination) into textur

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Paul, Angshuman, Dipti Prasad Mukherjee, Prasun Das, Abhinandan Gangopadhyay, Appa Rao Chintha, and Saurabh Kundu. "Improved Random Forest for Classification." IEEE Transactions on Image Processing 27, no. 8 (2018): 4012–24. http://dx.doi.org/10.1109/tip.2018.2834830.

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Razooq, Mohammed M., and Md Jan Nordin. "Texture Classification Using Random Forest." Advanced Science Letters 20, no. 10 (2014): 1918–21. http://dx.doi.org/10.1166/asl.2014.5649.

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Rustam, Zuherman, and Glori Stephani Saragih. "Prediction schizophrenia using random forest." TELKOMNIKA (Telecommunication Computing Electronics and Control) 18, no. 3 (2020): 1433. http://dx.doi.org/10.12928/telkomnika.v18i3.14837.

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Yu, Ying, Lingyun Wang, Hao Huang, and Wenwen Yang. "An Improved Random Forest Algorithm." Journal of Physics: Conference Series 1646 (September 2020): 012070. http://dx.doi.org/10.1088/1742-6596/1646/1/012070.

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McDonald, Anthony D., John D. Lee, Chris Schwarz, and Timothy L. Brown. "Steering in a Random Forest." Human Factors: The Journal of the Human Factors and Ergonomics Society 56, no. 5 (2013): 986–98. http://dx.doi.org/10.1177/0018720813515272.

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Utkin, Lev V., Andrei V. Konstantinov, Viacheslav S. Chukanov, Mikhail V. Kots, Mikhail A. Ryabinin, and Anna A. Meldo. "A weighted random survival forest." Knowledge-Based Systems 177 (August 2019): 136–44. http://dx.doi.org/10.1016/j.knosys.2019.04.015.

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