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Journal articles on the topic 'And random forests'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Bagui, Sikha, and Timothy Bennett. "Optimizing Random Forests: Spark Implementations of Random Genetic Forests." BOHR International Journal of Engineering 1, no. 1 (2022): 44–52. http://dx.doi.org/10.54646/bije.009.

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The Random Forest (RF) algorithm, originally proposed by Breiman [7], is a widely used machine learning algorithm that gains its merit from its fast learning speed as well as high classification accuracy. However, despite its widespread use, the different mechanisms at work in Breiman’s RF are not yet fully understood, and there is still on-going research on several aspects of optimizing the RF algorithm, especially in the big data environment. To optimize the RF algorithm, this work builds new ensembles that optimize the random portions of the RF algorithm using genetic algorithms, yielding R

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2

Bagui, Sikha, and Timothy Bennett. "Optimizing random forests: spark implementations of random genetic forests." BOHR International Journal of Engineering 1, no. 1 (2022): 42–51. http://dx.doi.org/10.54646/bije.2022.09.

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The Random Forest (RF) algorithm, originally proposed by Breiman et al. (1), is a widely used machine learning algorithm that gains its merit from its fast learning speed as well as high classification accuracy. However, despiteits widespread use, the different mechanisms at work in Breiman’s RF are not yet fully understood, and there is stillon-going research on several aspects of optimizing the RF algorithm, especially in the big data environment. To optimize the RF algorithm, this work builds new ensembles that optimize the random portions of the RF algorithm using genetic algorithms, yield

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3

Mantero, Alejandro, and Hemant Ishwaran. "Unsupervised random forests." Statistical Analysis and Data Mining: The ASA Data Science Journal 14, no. 2 (2021): 144–67. http://dx.doi.org/10.1002/sam.11498.

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4

Martin, James B., and Dominic Yeo. "Critical random forests." Latin American Journal of Probability and Mathematical Statistics 15, no. 2 (2018): 913. http://dx.doi.org/10.30757/alea.v15-35.

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5

Dmitry Devyatkin, A., and G. Oleg Grigoriev. "Random Kernel Forests." IEEE Access 10 (2022): 77962–79. http://dx.doi.org/10.1109/access.2022.3193385.

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6

Guelman, Leo, Montserrat Guillén, and Ana M. Pérez-Marín. "Uplift Random Forests." Cybernetics and Systems 46, no. 3-4 (2015): 230–48. http://dx.doi.org/10.1080/01969722.2015.1012892.

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7

Athey, Susan, Julie Tibshirani, and Stefan Wager. "Generalized random forests." Annals of Statistics 47, no. 2 (2019): 1148–78. http://dx.doi.org/10.1214/18-aos1709.

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8

Bernard, Simon, Sébastien Adam, and Laurent Heutte. "Dynamic Random Forests." Pattern Recognition Letters 33, no. 12 (2012): 1580–86. http://dx.doi.org/10.1016/j.patrec.2012.04.003.

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9

Taylor, Jeremy M. G. "Random Survival Forests." Journal of Thoracic Oncology 6, no. 12 (2011): 1974–75. http://dx.doi.org/10.1097/jto.0b013e318233d835.

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10

Ishwaran, Hemant, Udaya B. Kogalur, Eugene H. Blackstone, and Michael S. Lauer. "Random survival forests." Annals of Applied Statistics 2, no. 3 (2008): 841–60. http://dx.doi.org/10.1214/08-aoas169.

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11

Amaratunga, D., J. Cabrera, and Y. S. Lee. "Enriched random forests." Bioinformatics 24, no. 18 (2008): 2010–14. http://dx.doi.org/10.1093/bioinformatics/btn356.

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12

Biau, Gérard, Erwan Scornet, and Johannes Welbl. "Neural Random Forests." Sankhya A 81, no. 2 (2018): 347–86. http://dx.doi.org/10.1007/s13171-018-0133-y.

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13

Balińska, Krystyna T., Louis V. Quintas, and Jerzy Szymański. "Random recursive forests." Random Structures and Algorithms 5, no. 1 (1994): 3–12. http://dx.doi.org/10.1002/rsa.3240050103.

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14

Pitman, Jim. "Coalescent Random Forests." Journal of Combinatorial Theory, Series A 85, no. 2 (1999): 165–93. http://dx.doi.org/10.1006/jcta.1998.2919.

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15

Broutin, Nicolas, Luc Devroye, Gábor Lugosi, and Roberto Imbuzeiro Oliveira. "Subtractive random forests." Latin American Journal of Probability and Mathematical Statistics 21, no. 1 (2024): 575. http://dx.doi.org/10.30757/alea.v21-23.

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16

Segal, Mark, and Yuanyuan Xiao. "Multivariate random forests." WIREs Data Mining and Knowledge Discovery 1, no. 1 (2011): 80–87. http://dx.doi.org/10.1002/widm.12.

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17

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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18

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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19

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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20

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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21

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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22

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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23

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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24

Sun, Jianyuan, Guoqiang Zhong, Kaizhu Huang, and Junyu Dong. "Banzhaf random forests: Cooperative game theory based random forests with consistency." Neural Networks 106 (October 2018): 20–29. http://dx.doi.org/10.1016/j.neunet.2018.06.006.

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25

Goehry, Benjamin. "Random forests for time-dependent processes." ESAIM: Probability and Statistics 24 (2020): 801–26. http://dx.doi.org/10.1051/ps/2020015.

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Random forests were introduced by Breiman in 2001. We study theoretical aspects of both original Breiman’s random forests and a simplified version, the centred random forests. Under the independent and identically distributed hypothesis, Scornet, Biau and Vert proved the consistency of Breiman’s random forest, while Biau studied the simplified version and obtained a rate of convergence in the sparse case. However, the i.i.d hypothesis is generally not satisfied for example when dealing with time series. We extend the previous results to the case where observations are weakly dependent, more pr

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26

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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27

Désir, Chesner, Simon Bernard, Caroline Petitjean, and Laurent Heutte. "One class random forests." Pattern Recognition 46, no. 12 (2013): 3490–506. http://dx.doi.org/10.1016/j.patcog.2013.05.022.

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28

Seyedhosseini, Mojtaba, and Tolga Tasdizen. "Disjunctive normal random forests." Pattern Recognition 48, no. 3 (2015): 976–83. http://dx.doi.org/10.1016/j.patcog.2014.08.023.

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29

Xu, Ruo, Dan Nettleton, and Daniel J. Nordman. "Case-Specific Random Forests." Journal of Computational and Graphical Statistics 25, no. 1 (2016): 49–65. http://dx.doi.org/10.1080/10618600.2014.983641.

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30

Karlsson, Isak, Panagiotis Papapetrou, and Henrik Boström. "Generalized random shapelet forests." Data Mining and Knowledge Discovery 30, no. 5 (2016): 1053–85. http://dx.doi.org/10.1007/s10618-016-0473-y.

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31

Scornet, Erwan, Gérard Biau, and Jean-Philippe Vert. "Consistency of random forests." Annals of Statistics 43, no. 4 (2015): 1716–41. http://dx.doi.org/10.1214/15-aos1321.

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32

Jaeger, Byron C., D. Leann Long, Dustin M. Long, et al. "Oblique random survival forests." Annals of Applied Statistics 13, no. 3 (2019): 1847–83. http://dx.doi.org/10.1214/19-aoas1261.

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33

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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34

Sadorsky, Perry. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers." Journal of Risk and Financial Management 14, no. 5 (2021): 198. http://dx.doi.org/10.3390/jrfm14050198.

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Gold is often used by investors as a hedge against inflation or adverse economic times. Consequently, it is important for investors to have accurate forecasts of gold prices. This paper uses several machine learning tree-based classifiers (bagging, stochastic gradient boosting, random forests) to predict the price direction of gold and silver exchange traded funds. Decision tree bagging, stochastic gradient boosting, and random forests predictions of gold and silver price direction are much more accurate than those obtained from logit models. For a 20-day forecast horizon, tree bagging, stocha

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35

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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36

Kilpatrick, Alexander James, Aleksandra Ćwiek, and Shigeto Kawahara. "Random forests, sound symbolism and Pokémon evolution." PLOS ONE 18, no. 1 (2023): e0279350. http://dx.doi.org/10.1371/journal.pone.0279350.

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This study constructs machine learning algorithms that are trained to classify samples using sound symbolism, and then it reports on an experiment designed to measure their understanding against human participants. Random forests are trained using the names of Pokémon, which are fictional video game characters, and their evolutionary status. Pokémon undergo evolution when certain in-game conditions are met. Evolution changes the appearance, abilities, and names of Pokémon. In the first experiment, we train three random forests using the sounds that make up the names of Japanese, Chinese, and K

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37

Kulyukin, Vladimir, Nikhil Ganta, and Anastasiia Tkachenko. "On Image Classification in Video Analysis of Omnidirectional Apis Mellifera Traffic: Random Reinforced Forests vs. Shallow Convolutional Networks." Applied Sciences 11, no. 17 (2021): 8141. http://dx.doi.org/10.3390/app11178141.

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Omnidirectional honeybee traffic is the number of bees moving in arbitrary directions in close proximity to the landing pad of a beehive over a period of time. Automated video analysis of such traffic is critical for continuous colony health assessment. In our previous research, we proposed a two-tier algorithm to measure omnidirectional bee traffic in videos. Our algorithm combines motion detection with image classification: in tier 1, motion detection functions as class-agnostic object location to generate regions with possible objects; in tier 2, each region from tier 1 is classified by a c

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38

RAGHUNANDA, SUCHETA. "Error Classification based on Multinomial NB and Random Forests." Journal of Research on the Lepidoptera 51, no. 2 (2020): 687–95. http://dx.doi.org/10.36872/lepi/v51i2/301127.

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39

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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40

Prinzie, Anita, and Dirk Van den Poel. "Random Forests for multiclass classification: Random MultiNomial Logit." Expert Systems with Applications 34, no. 3 (2008): 1721–32. http://dx.doi.org/10.1016/j.eswa.2007.01.029.

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41

Contat, Alice, Jean-François Delmas, Jean-Jil Duchamps, Igor Kortchemski, and Michel Nassif. "On random trees and forests." ESAIM: Proceedings and Surveys 74 (November 2023): 19–37. http://dx.doi.org/10.1051/proc/202374019.

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The first talk at the session Random trees and random forests “Journée MAS” (27/08/2021) was presented by I. Kortchemski. After a general up-to-date introduction to local and scaling limits of Bienaymé trees (which are discrete branching trees), he presented new results on precise behavior of the largest out-degree of large branching trees when the offspring distribution μ is subcritical with μ(n) of order n−β for large n and β > 2 or critical with μ(n) of order n−2. In the next talk, M. Nassif gave asymptotics of additive functionals of large Bienayme trees in the global regime, which can

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42

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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43

Zhong, Yuan, Hongyu Yang, Yanci Zhang, and Ping Li. "Online Rebuilding Regression Random Forests." Knowledge-Based Systems 221 (June 2021): 106960. http://dx.doi.org/10.1016/j.knosys.2021.106960.

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44

Xu, Ruo, Dan Nettleton, and Daniel J. Nordman. "Predictor augmentation in random forests." Statistics and Its Interface 7, no. 2 (2014): 177–86. http://dx.doi.org/10.4310/sii.2014.v7.n2.a3.

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45

Abdulsalam, Hanady, David B. Skillicorn, and Patrick Martin. "Classification Using Streaming Random Forests." IEEE Transactions on Knowledge and Data Engineering 23, no. 1 (2011): 22–36. http://dx.doi.org/10.1109/tkde.2010.36.

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46

Zhang, Heng-Ru, Fan Min, and Xu He. "Aggregated Recommendation through Random Forests." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/649596.

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Aggregated recommendation refers to the process of suggesting one kind of items to a group of users. Compared to user-oriented or item-oriented approaches, it is more general and, therefore, more appropriate for cold-start recommendation. In this paper, we propose a random forest approach to create aggregated recommender systems. The approach is used to predict the rating of a group of users to a kind of items. In the preprocessing stage, we merge user, item, and rating information to construct an aggregated decision table, where rating information serves as the decision attribute. We also mod

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47

Scornet, Erwan. "Tuning parameters in random forests." ESAIM: Proceedings and Surveys 60 (2017): 144–62. http://dx.doi.org/10.1051/proc/201760144.

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48

Scornet, Erwan. "Random Forests and Kernel Methods." IEEE Transactions on Information Theory 62, no. 3 (2016): 1485–500. http://dx.doi.org/10.1109/tit.2016.2514489.

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49

Genuer, Robin, Jean-Michel Poggi, and Christine Tuleau-Malot. "Variable selection using random forests." Pattern Recognition Letters 31, no. 14 (2010): 2225–36. http://dx.doi.org/10.1016/j.patrec.2010.03.014.

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Ishwaran, Hemant, and Udaya B. Kogalur. "Consistency of random survival forests." Statistics & Probability Letters 80, no. 13-14 (2010): 1056–64. http://dx.doi.org/10.1016/j.spl.2010.02.020.

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