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Artículos de revistas sobre el tema "Random forest"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 7 de mayo de 2025

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1

Mantas, Carlos J., Javier G. Castellano, Serafín Moral-García y Joaquín Abellán. "A comparison of random forest based algorithms: random credal random forest versus oblique random forest". Soft Computing 23, n.º 21 (17 de noviembre de 2018): 10739–54. http://dx.doi.org/10.1007/s00500-018-3628-5.

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2

Rigatti, Steven J. "Random Forest". Journal of Insurance Medicine 47, n.º 1 (1 de enero de 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 technique which uses bootstrap aggregation and randomization of predictors to achieve a high degree of predictive accuracy. The various input parameters of the random forest are explored. Colon cancer data (n = 66,807) from the SEER database is then used to construct both a Cox model and a random forest model to determine how well the models perform on the same data. Both models perform well, achieving a concordance error rate of approximately 18%.

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3

Yamaoka, Keisuke. "Random Forest". Journal of The Institute of Image Information and Television Engineers 66, n.º 7 (2012): 573–75. http://dx.doi.org/10.3169/itej.66.573.

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4

x, Adeen y Preeti Sondhi. "Random Forest Based Heart Disease Prediction". International Journal of Science and Research (IJSR) 10, n.º 2 (27 de febrero de 2021): 1669–72. https://doi.org/10.21275/sr21225214148.

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MISHINA, Yohei, Ryuei MURATA, Yuji YAMAUCHI, Takayoshi YAMASHITA y Hironobu FUJIYOSHI. "Boosted Random Forest". IEICE Transactions on Information and Systems E98.D, n.º 9 (2015): 1630–36. http://dx.doi.org/10.1587/transinf.2014opp0004.

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6

Han, Sunwoo, Hyunjoong Kim y Yung-Seop Lee. "Double random forest". Machine Learning 109, n.º 8 (2 de julio de 2020): 1569–86. http://dx.doi.org/10.1007/s10994-020-05889-1.

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7

Cho, Yunsub, Soowoong Jeong y Sangkeun Lee. "Positive Random Forest based Robust Object Tracking". Journal of the Institute of Electronics and Information Engineers 52, n.º 6 (25 de junio de 2015): 107–16. http://dx.doi.org/10.5573/ieie.2015.52.6.107.

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8

Wagle, Aumkar. "Random Forest Classifier to Predict Financial Data". International Journal of Science and Research (IJSR) 13, n.º 4 (5 de abril de 2024): 1932–43. http://dx.doi.org/10.21275/sr24418155701.

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9

Salman, Hasan Ahmed, Ali Kalakech y Amani Steiti. "Random Forest Algorithm Overview". Babylonian Journal of Machine Learning 2024 (8 de junio de 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 processing methods enhances precision and innovation in problem-solving. Utilizing diverse methodologies in interdisciplinary research streamlines the research process, fosters innovation, and enables the application of data analysis findings to pattern recognition, decision-making, predictive modeling, and problem-solving. This approach also encourages in-novation in interdisciplinary research. This technique utilizes the concept of decision trees, con-structing a collection of decision trees and aggregating their outcomes to generate the ultimate prediction. Every decision tree inside a random forest is constructed using random subsets of data, and each individual tree is trained on a portion of the whole dataset. Subsequently, the outcomes of all decision trees are amalgamated to derive the ultimate forecast. One of the bene-fits of random forests is their capacity to handle unbalanced data and variables with missing values. Additionally, it mitigates the issue of arbitrary variable selection seen by certain alterna-tive models. Furthermore, random forests mitigate the issue of overfitting by training several de-cision trees on random subsets of data, hence enhancing their ability to generalize to novel data. Random forests are highly regarded as one of the most efficient and potent techniques in the domain of machine learning. They find extensive use in various applications such as automatic categorization, data forecasting, and supervisory learning.

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10

LIU, Zhi, Zhaocai SUN y Hongjun WANG. "Specific Random Trees for Random Forest". IEICE Transactions on Information and Systems E96.D, n.º 3 (2013): 739–41. http://dx.doi.org/10.1587/transinf.e96.d.739.

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11

Sulistiyono, N., C. P. Tarigan, A. F. Daulay, S. A. Hudjimartsu y 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, n.º 1 (1 de mayo de 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 maximum likelihood and random forest method. The results showed that random forest methods are more accurate than the maximum likelihood of identifying mangrove forests in Medan Belawan. The kappa accuracy value of the digital satellite image classification using the random forest method is 85.09%, and the maximum likelihood method is 77.4%. Based on the random forest method, the mangrove forest area is 1,325.81 hectares or 43.64% of the Belawan District area.

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12

Puerta, Ronald y José Iannacone. "Analysis of forest cover in Parque Nacional Tingo María (Peru) using the random forest algorithm". Scientia Agropecuaria 14, n.º 3 (11 de agosto de 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 were obtained for the study area corresponding to the years 2017, 2019, 2021 and 2023, achieving considerable thematic accuracy. During the evaluation periods, the rates of change from forest to non-forest within the PNTM presented low values -0.26% (2017 - 2019); -1.24% (2019 - 2021) and -0.02% (2021 - 2023). While the forests in the ZA have undergone a dynamic transition, with rates of change of -2.97%; -4.39% and -1.15% derived from land use change. The landscape metrics suggest that the forests of the PNTM are moderately fragmented, and the forests of the ZA are strongly fragmented, which leads to the conclusion that the protected natural area has fulfilled its objective of maintaining vegetation cover.

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13

Roy, Marie-Hélène y Denis Larocque. "Prediction intervals with random forests". Statistical Methods in Medical Research 29, n.º 1 (21 de febrero de 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) paradigm and the transformation forest method. For CART forests, in addition to the default least-squares splitting rule, two alternative splitting criteria are investigated. We also present and evaluate the performance of five flexible methods for constructing prediction intervals. This yields 20 distinct method variations. To reliably attain the desired confidence level, we include a calibration procedure performed on the out-of-bag information provided by the forest. The 20 method variations are thoroughly investigated, and compared to five alternative methods through simulation studies and in real data settings. The results show that the proposed methods are very competitive. They outperform commonly used methods in both in simulation settings and with real data.

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14

Zhang, Gongqiao y Gangying Hui. "Random Trees Are the Cornerstones of Natural Forests". Forests 12, n.º 8 (6 de agosto de 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. Several variables and their distributions were applied to study community characteristics comprehensively, including species abundance, diameter distribution, spatial pattern, mingling, crowding, and competition. The genetic absolute distance method is used to analyze the similarity between the random trees and the communities. Our results show that the features of random trees are highly consistent with the communities. The study proposes that random trees are the cornerstones of natural forests. Its quantitative advantage explains the key role that random trees play in natural forests. The study could provide a scientific insight into the protection, monitoring, and management of forests.

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15

Mathew, Dr Tina Elizabeth. "An Improvised Random Forest Model for Breast Cancer Classification". NeuroQuantology 20, n.º 5 (18 de mayo de 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 that helps in better classification. Random Forests are applied to the Wisconsin breast cancer dataset and the performance of the classifier is evaluated for breast cancer classification. Here in this study an improvised random forest model which uses a cost sensitive learning approach for classification is proposed and it is found to have a better performance than the traditional random forest approach. The model gave an accuracy of 97.51%.

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16

Dai, Hongsheng. "Perfect sampling methods for random forests". Advances in Applied Probability 40, n.º 3 (septiembre de 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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17

Yu, Tianyu, Cuiwei Liu, Zhuo Yan y Xiangbin Shi. "A Multi-Task Framework for Action Prediction". Information 11, n.º 3 (16 de marzo de 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 and multi-task random forests. Unlike the traditional single-task random forests, multi-task random forests are built upon incomplete training videos annotated with action labels as well as temporal progress labels. Meanwhile, incorporating both random forests and multi-task random forests can increase the diversity of classifiers and improve the discriminative power of the multi-task deep forest. Experiments on the UT-Interaction and the BIT-Interaction datasets demonstrate the effectiveness of the proposed multi-task deep forest.

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18

Koptev, Sergey V. y Hasan Alabdullahalhasno. "Applying the Random Forest Algorithm to Analyze the Dynamics of Taiga-Tundra Forest Ecosystems". Lesnoy Zhurnal (Forestry Journal), n.º 2 (5 de abril de 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 restoration of non-forest covered areas, as well as their advancement northward taking-up lands due to climate change. The evaluation of the accuracy of automated classification of satellite images using the Random Forest algorithm by comparing them with reference data using criteria such as overall accuracy and the Kappa coefficient (the degree of correspondence between the model estimates and the actual data) has confirmed the reliability of the results obtained. The benchmarks have been taken from inventory databases, stationary sample plot data and the state forest inventory data. Before starting field work, cartographic databases have been studied and sample plots have been selected. Based on experimental data for the study area, a large number of polygons have been created, reflecting the diversity of forest stands and non-forest areas, to train the algorithm for classifying satellite images. Image processing, including corrections, mosaics, geoprojection and return, has been performed using SNAP (Sentinel Application Platform), an open source program. 100 points in various forest vegetation conditions in the study area have been analyzed. Studying the dynamics of forest ecosystems based on the Earth remote sensing data and the application of image classification using the Random Forest algorithm will improve the accuracy of assessing the resource and environmental potential of northern taiga and tundra forests of the Arkhangelsk Region.

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19

Dai, Hongsheng. "Perfect sampling methods for random forests". Advances in Applied Probability 40, n.º 03 (septiembre de 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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20

Dumarevskaya, Liubov y Jason R. Parent. "Modeling Spongy Moth Forest Mortality in Rhode Island Temperate Deciduous Forest". Forests 16, n.º 1 (8 de enero de 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 water. Random Forest was used to model forest mortality with two classes (low/high) and three classes (low/med/high). The best models had overall accuracies of 82% and 65% for the two-class and three-class models, respectively. The most important predictors of forest mortality were defoliation, distance to coast, and canopy cover. Model performance improved only slightly with the inclusion of more than three variables. The models classified 35% of forests as having canopy mortality >5 trees/ha and 21% of Rhode Island forests having mortality >11 trees/ha. The study shows the benefit of Random Forest models that use both defoliation maps and geospatial environmental data for classifying forest mortality caused by Spongy moth.

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21

Sekulić, Aleksandar, Milan Kilibarda, Gerard B. M. Heuvelink, Mladen Nikolić y Branislav Bajat. "Random Forest Spatial Interpolation". Remote Sensing 12, n.º 10 (25 de mayo de 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 into account in machine learning, but so far, none of these approaches have taken the natural route of incorporating the nearest observations and their distances to the prediction location as covariates. In this research, we explored the value of including observations at the nearest locations and their distances from the prediction location by introducing Random Forest Spatial Interpolation (RFSI). We compared RFSI with deterministic interpolation methods, ordinary kriging, regression kriging, Random Forest and Random Forest for spatial prediction (RFsp) in three case studies. The first case study made use of synthetic data, i.e., simulations from normally distributed stationary random fields with a known semivariogram, for which ordinary kriging is known to be optimal. The second and third case studies evaluated the performance of the various interpolation methods using daily precipitation data for the 2016–2018 period in Catalonia, Spain, and mean daily temperature for the year 2008 in Croatia. Results of the synthetic case study showed that RFSI outperformed most simple deterministic interpolation techniques and had similar performance as inverse distance weighting and RFsp. As expected, kriging was the most accurate technique in the synthetic case study. In the precipitation and temperature case studies, RFSI mostly outperformed regression kriging, inverse distance weighting, random forest, and RFsp. Moreover, RFSI was substantially faster than RFsp, particularly when the training dataset was large and high-resolution prediction maps were made.

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22

Goel, Eesha y Er Abhilasha. "Random Forest: A Review". International Journal of Advanced Research in Computer Science and Software Engineering 7, n.º 1 (30 de enero de 2017): 251–57. http://dx.doi.org/10.23956/ijarcsse/v7i1/01113.

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23

Zhang, Haozhe, Joshua Zimmerman, Dan Nettleton y Daniel J. Nordman. "Random Forest Prediction Intervals". American Statistician 74, n.º 4 (4 de junio de 2019): 392–406. http://dx.doi.org/10.1080/00031305.2019.1585288.

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24

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

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

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26

Bonissone, Piero, José M. Cadenas, M. Carmen Garrido y R. Andrés Díaz-Valladares. "A fuzzy random forest". International Journal of Approximate Reasoning 51, n.º 7 (septiembre de 2010): 729–47. http://dx.doi.org/10.1016/j.ijar.2010.02.003.

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27

Akay, Ebru Çaglayan, Kadriye Hilal Topal, Saban Kizilarslan y Hoseng Bulbul. "Forecasting of Turkish housing price index: ARIMA, random forest, ARIMA-random forest". Pressacademia 10, n.º 10 (30 de diciembre de 2019): 7–11. http://dx.doi.org/10.17261/pressacademia.2019.1134.

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28

Kumano, So y Tatsuya Akutsu. "Comparison of the Representational Power of Random Forests, Binary Decision Diagrams, and Neural Networks". Neural Computation 34, n.º 4 (23 de marzo de 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 equivalent shallow depth neural network with a polynomial number of nodes. These results suggest that even shallow neural networks have the same or higher representation power than deep random forests and deep BDDs. We also show that in some cases, an exponential number of nodes are required to express a given random forest by a random forest with a much fewer number of trees, which suggests that many trees are required for random forests to represent some specific knowledge efficiently.

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Novikova, Tatyana, Svetlana Evdokimova y Gotsui Wu. "Development of a quantitative investment algorithm based on Random Forest". Modeling of systems and processes 15, n.º 4 (13 de diciembre de 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 training sets that build their own calculation models, and then their results are summed and integrated to obtain the final prediction. The randomness of Random Forest comes into play in the process of selecting samples from the training dataset and in selecting features to calculate the best split points. However, the proposed strategy is more stable than other stock selection strategies, is more suitable for building quantitative stock selection models, the proposed algorithm has an advantage over other algorithms, and is also more promising for further development.

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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, n.º 11 (5 de noviembre de 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, n.º 1 (1 de febrero de 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 error of the random forest. This analysis is expected to enrich the literature of random forests by providing further insight into its working mechanism. These insights will assist the practitioners of the random forest implement this algorithm more wisely and in an informed way. DUJASE Vol. 7(1) 67-71, 2022 (January)

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Xin Sui, Xin Sui, Hailong Zhao Xin Sui, Honghua Xu Hailong Zhao, Xiaolong Song Honghua Xu y Dan Liu Xiaolong Song. "Load Forecasting Based on Optimized Random Forest Algorithm in Cloud Environment". 電腦學刊 35, n.º 3 (junio de 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 optimized by genetic algorithm proposed in this paper has higher forecasting accuracy.</p> <p> </p>

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Isidoros, Iakovidis y Nicola Arcozzi. "Improved convergence rates for some kernel random forest algorithms". Mathematics in Engineering 6, n.º 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 we explore the related reproducing kernel Hilbert space defined by the explicit kernel of the centered random forest.</p></abstract>

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Mascaro, Joseph, Gregory P. Asner, David E. Knapp, Ty Kennedy-Bowdoin, Roberta E. Martin, Christopher Anderson, Mark Higgins y K. Dana Chadwick. "A Tale of Two “Forests”: Random Forest Machine Learning Aids Tropical Forest Carbon Mapping". PLoS ONE 9, n.º 1 (28 de enero de 2014): e85993. http://dx.doi.org/10.1371/journal.pone.0085993.

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35

Łuczak, Tomasz y Boris Pittel. "Components of Random Forests". Combinatorics, Probability and Computing 1, n.º 1 (marzo de 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 times slower than the largest component of G(n, M) and the second largest tree in ℱ(n, M) is of the order n⅔ for every M = n/2 +s, provided that s3n−2 → ∞ and s = o(n), while its counterpart in G(n, M) is of the order n2s−2 log(s3n−2) ≪ n⅔.

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36

Vacek, Zdeněk, Stanislav Vacek, Lukáš Bílek, Jan Král, Iva Ulbrichová, Jaroslav Simon y Daniel Bulušek. "Impact of applied silvicultural systems on spatial pattern of hornbeam-oak forests". Central European Forestry Journal 64, n.º 1 (1 de marzo de 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. Horizontal structure of the tree stems of the studied tree species in high forest was random, in oak it was moderately regular. In coppice with standards it was random in oak, in hornbeam and linden it was aggregated within 3 – 5 m and random up to a larger spacing. In low forest at a distance of 4 – 6 m the horizontal structure of the three studied tree species was aggregated while it was random at a larger spacing. The horizontal structure of natural regeneration was aggregated in all forest types. In coppice with standards and high forest, parent stand had significant negative effect on the natural regeneration at smaller distance (to 1.4 m from the stem). Crown centroids were more regularly distributed than tree stems, especially in low forest (2.0 m) and in linden (2.3 m). Our results contribute to existing knowledge about silvicultural systems and their impact on hornbeam-oak forests with implications for forest management and nature protection.

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Huang, Tianbao, Guanglong Ou, Yong Wu, Xiaoli Zhang, Zihao Liu, Hui Xu, Xiongwei Xu, Zhenghui Wang y Can Xu. "Estimating the Aboveground Biomass of Various Forest Types with High Heterogeneity at the Provincial Scale Based on Multi-Source Data". Remote Sensing 15, n.º 14 (14 de julio de 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 2A images. The results showed that (1) DEM was the most important variable for estimating the AGB of coniferous forests, evergreen broadleaved forests, deciduous broadleaved forests, and mixed forests; while the vegetation index was the most important variable for estimating deciduous broadleaved forests, the climatic factors had a higher variable importance for estimating coniferous and mixed forests, and texture features and vegetation index had a higher variable importance for estimating evergreen broadleaved forests. (2) In terms of specific model performance for the four forest types, RRF was the best model both in estimating the AGB of coniferous forests and mixed forests; the R2 and RMSE for coniferous forests were 0.63 and 43.23 Mg ha−1, respectively, and the R2 and RMSE for mixed forests were 0.56 and 47.79 Mg ha−1, respectively. BRNN performed the best in estimating the AGB of evergreen broadleaved forests; the R2 was 0.53 and the RMSE was 68.16 Mg ha−1. QRF was the best in estimating the AGB of deciduous broadleaved forests, with R2 of 0.43 and RMSE of 45.09 Mg ha−1. (3) RRF was the best model for the four forest types according to the mean values, with R2 and RMSE of 0.503 and 52.335 Mg ha−1, respectively. In conclusion, different variables and suitable models should be considered when estimating the AGB of different forest types. This study could provide a reference for the estimation of forest AGB based on remote sensing in complex terrain areas with a high degree of forest heterogeneity.

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Kwon, Hyeokjun y Jonghan Sea. "Characteristics of Sexual Homicide: Based on Random Forest Analysis". KOREAN CRIMINOLOGICAL REVIEW 33, n.º 1 (31 de marzo de 2022): 165–92. http://dx.doi.org/10.36889/kcr.2022.3.31.1.165.

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39

Khadar Nawas, K., Manish Kumar Barik y 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 obtained from the Random Forest classifier shows promising result. It is observed that the accuracy level is significantly higher in MFCC as compared to the RPS technique on the data taken from the well-known TIMIT corpus dataset.

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40

Zhao, Zi Ming, Cui Hua Li, Hua Shi y Quan Zou. "Material Classification Using Random Forest". Advanced Materials Research 301-303 (julio de 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 texture classes. We carry out experiments on Columbia-Utrecht database. The experimental results show that our method successfully solves plain texture classification problem with high computational efficiency.

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41

Paul, Angshuman, Dipti Prasad Mukherjee, Prasun Das, Abhinandan Gangopadhyay, Appa Rao Chintha y Saurabh Kundu. "Improved Random Forest for Classification". IEEE Transactions on Image Processing 27, n.º 8 (agosto de 2018): 4012–24. http://dx.doi.org/10.1109/tip.2018.2834830.

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42

Razooq, Mohammed M. y Md Jan Nordin. "Texture Classification Using Random Forest". Advanced Science Letters 20, n.º 10 (1 de octubre de 2014): 1918–21. http://dx.doi.org/10.1166/asl.2014.5649.

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43

Rustam, Zuherman y Glori Stephani Saragih. "Prediction schizophrenia using random forest". TELKOMNIKA (Telecommunication Computing Electronics and Control) 18, n.º 3 (1 de junio de 2020): 1433. http://dx.doi.org/10.12928/telkomnika.v18i3.14837.

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44

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

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45

McDonald, Anthony D., John D. Lee, Chris Schwarz y Timothy L. Brown. "Steering in a Random Forest". Human Factors: The Journal of the Human Factors and Ergonomics Society 56, n.º 5 (12 de diciembre de 2013): 986–98. http://dx.doi.org/10.1177/0018720813515272.

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46

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

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47

Biau, Gérard y Erwan Scornet. "A random forest guided tour". TEST 25, n.º 2 (19 de abril de 2016): 197–227. http://dx.doi.org/10.1007/s11749-016-0481-7.

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48

Wang, Suhang, Charu Aggarwal y Huan Liu. "Random-Forest-Inspired Neural Networks". ACM Transactions on Intelligent Systems and Technology 9, n.º 6 (15 de noviembre de 2018): 1–25. http://dx.doi.org/10.1145/3232230.

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Boikov, A. V., R. V. Savelev y V. A. Payor. "DEM Calibration Approach: Random Forest". Journal of Physics: Conference Series 1118 (diciembre de 2018): 012009. http://dx.doi.org/10.1088/1742-6596/1118/1/012009.

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50

Hatwell, Julian, Mohamed Medhat Gaber y R. Muhammad Atif Azad. "CHIRPS: Explaining random forest classification". Artificial Intelligence Review 53, n.º 8 (4 de junio de 2020): 5747–88. http://dx.doi.org/10.1007/s10462-020-09833-6.

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Abstract Modern machine learning methods typically produce “black box” models that are opaque to interpretation. Yet, their demand has been increasing in the Human-in-the-Loop processes, that is, those processes that require a human agent to verify, approve or reason about the automated decisions before they can be applied. To facilitate this interpretation, we propose Collection of High Importance Random Path Snippets (CHIRPS); a novel algorithm for explaining random forest classification per data instance. CHIRPS extracts a decision path from each tree in the forest that contributes to the majority classification, and then uses frequent pattern mining to identify the most commonly occurring split conditions. Then a simple, conjunctive form rule is constructed where the antecedent terms are derived from the attributes that had the most influence on the classification. This rule is returned alongside estimates of the rule’s precision and coverage on the training data along with counter-factual details. An experimental study involving nine data sets shows that classification rules returned by CHIRPS have a precision at least as high as the state of the art when evaluated on unseen data (0.91–0.99) and offer a much greater coverage (0.04–0.54). Furthermore, CHIRPS uniquely controls against under- and over-fitting solutions by maximising novel objective functions that are better suited to the local (per instance) explanation setting.

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