Relevant bibliographies by topics / Random forest

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Random forest'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 May 2025

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Journal articles on the topic "Random forest"

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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 (November 17, 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 (January 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 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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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 (February 27, 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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Han, Sunwoo, Hyunjoong Kim, and Yung-Seop Lee. "Double random forest." Machine Learning 109, no. 8 (July 2, 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 (June 25, 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 (April 5, 2024): 1932–43. http://dx.doi.org/10.21275/sr24418155701.

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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 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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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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Dissertations / Theses on the topic "Random forest"

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Linusson, Henrik, Robin Rudenwall, and Andreas Olausson. "Random forest och glesa datarespresentationer." Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-16672.

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In silico experimentation is the process of using computational and statistical models to predict medicinal properties in chemicals; as a means of reducing lab work and increasing success rate this process has become an important part of modern drug development. There are various ways of representing molecules - the problem that motivated this paper derives from collecting substructures of the chemical into what is known as fractional representations. Assembling large sets of molecules represented in this way will result in sparse data, where a large portion of the set is null values. This consumes an excessive amount of computer memory which inhibits the size of data sets that can be used when constructing predictive models.In this study, we suggest a set of criteria for evaluation of random forest implementations to be used for in silico predictive modeling on sparse data sets, with regard to computer memory usage, model construction time and predictive accuracy.A novel random forest system was implemented to meet the suggested criteria, and experiments were made to compare our implementation to existing machine learning algorithms to establish our implementation‟s correctness. Experimental results show that our random forest implementation can create accurate prediction models on sparse datasets, with lower memory usage overhead than implementations using a common matrix representation, and in less time than existing random forest implementations evaluated against. We highlight design choices made to accommodate for sparse data structures and data sets in the random forest ensemble technique, and therein present potential improvements to feature selection in sparse data sets.
Program: Systemarkitekturutbildningen
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Karlsson, Isak. "Order in the random forest." Doctoral thesis, Stockholms universitet, Institutionen för data- och systemvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-142052.

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In many domains, repeated measurements are systematically collected to obtain the characteristics of objects or situations that evolve over time or other logical orderings. Although the classification of such data series shares many similarities with traditional multidimensional classification, inducing accurate machine learning models using traditional algorithms are typically infeasible since the order of the values must be considered. In this thesis, the challenges related to inducing predictive models from data series using a class of algorithms known as random forests are studied for the purpose of efficiently and effectively classifying (i) univariate, (ii) multivariate and (iii) heterogeneous data series either directly in their sequential form or indirectly as transformed to sparse and high-dimensional representations. In the thesis, methods are developed to address the challenges of (a) handling sparse and high-dimensional data, (b) data series classification and (c) early time series classification using random forests. The proposed algorithms are empirically evaluated in large-scale experiments and practically evaluated in the context of detecting adverse drug events. In the first part of the thesis, it is demonstrated that minor modifications to the random forest algorithm and the use of a random projection technique can improve the effectiveness of random forests when faced with discrete data series projected to sparse and high-dimensional representations. In the second part of the thesis, an algorithm for inducing random forests directly from univariate, multivariate and heterogeneous data series using phase-independent patterns is introduced and shown to be highly effective in terms of both computational and predictive performance. Then, leveraging the notion of phase-independent patterns, the random forest is extended to allow for early classification of time series and is shown to perform favorably when compared to alternatives. The conclusions of the thesis not only reaffirm the empirical effectiveness of random forests for traditional multidimensional data but also indicate that the random forest framework can, with success, be extended to sequential data representations.
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Siegel, Kathryn I. (Kathryn Iris). "Incremental random forest classifiers in spark." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106105.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 53).
The random forest is a machine learning algorithm that has gained popularity due to its resistance to noise, good performance, and training efficiency. Random forests are typically constructed using a static dataset; to accommodate new data, random forests are usually regrown. This thesis presents two main strategies for updating random forests incrementally, rather than entirely rebuilding the forests. I implement these two strategies-incrementally growing existing trees and replacing old trees-in Spark Machine Learning(ML), a commonly used library for running ML algorithms in Spark. My implementation draws from existing methods in online learning literature, but includes several novel refinements. I evaluate the two implementations, as well as a variety of hybrid strategies, by recording their error rates and training times on four different datasets. My benchmarks show that the optimal strategy for incremental growth depends on the batch size and the presence of concept drift in a data workload. I find that workloads with large batches should be classified using a strategy that favors tree regrowth, while workloads with small batches should be classified using a strategy that favors incremental growth of existing trees. Overall, the system demonstrates significant efficiency gains when compared to the standard method of regrowing the random forest.
by Kathryn I. Siegel.
M. Eng.
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Cheng, Chuan. "Random forest training on reconfigurable hardware." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/28122.

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Random Forest (RF) is one of the most widely used supervised learning methods available. An RF is ensemble of decision tree classifiers with injection of several sources of randomness. It demonstrates a set of improvement over single decision and regression trees and is comparable or superior to major classification tools such as support vector machine (SVM) and adaptive boosting (Adaboost) with respect to accuracy, interpretability, robustness and processing speed. RF can be generally divided into training process and predicting process. Recently with emergence of large-scale data mining applications, the RF training process implemented in software on a single computer can no longer induce a complex RF model within reasonable amount of time. Alternative solutions involving computer clusters and GPUs usually come with disadvantages with respect to Performance/Power ratio and are not feasible for portable/embedded applications. In this work a set of FPGA-based implementations of the RF training process are proposed. FPGA devices allow construction of efficient custom hardware architectures and feature lower power consumption than typical GPPs or GPUs therefore are suitable for portable/embedded applications. The proposed hardware training architectures take advantage of different types of inherent parallelism in the RF training algorithm and distribute the workload to a set of parallel workers. Combining the parallel processing techniques with custom hardware designs featuring low latency, the architectures are able to accelerate the training process without loss in accuracy.
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Nelson, Marc. "Evaluating Multitemporal Sentinel-2 data for Forest Mapping using Random Forest." Thesis, Stockholms universitet, Institutionen för naturgeografi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-146657.

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The mapping of land cover using remotely sensed data is most effective when a robust classification method is employed. Random forest is a modern machine learning algorithm that has recently gained interest in the field of remote sensing due to its non-parametric nature, which may be better suited to handle complex, high-dimensional data than conventional techniques. In this study, the random forest method is applied to remote sensing data from the European Space Agency’s new Sentinel-2 satellite program, which was launched in 2015 yet remains relatively untested in scientific literature using non-simulated data. In a study site of boreo-nemoral forest in Ekerö mulicipality, Sweden, a classification is performed for six forest classes based on CadasterENV Sweden, a multi-purpose land covermapping and change monitoring program. The performance of Sentinel-2’s Multi-SpectralImager is investigated in the context of time series to capture phenological conditions, optimal band combinations, as well as the influence of sample size and ancillary inputs.Using two images from spring and summer of 2016, an overall map accuracy of 86.0% was achieved. The red edge, short wave infrared, and visible red bands were confirmed to be of high value. Important factors contributing to the result include the timing of image acquisition, use of a feature reduction approach to decrease the correlation between spectral channels, and the addition of ancillary data that combines topographic and edaphic information. The results suggest that random forest is an effective classification technique that is particularly well suited to high-dimensional remote sensing data.
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Lak, Kameran Majeed Mohammed <1985&gt. "Retina-inspired random forest for semantic image labelling." Master's Degree Thesis, Università Ca' Foscari Venezia, 2015. http://hdl.handle.net/10579/5970.

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One of the most challenging problem in computer vision community is semantic image labeling, which requires assigning a semantic class to each pixel in an image. In the literature, this problem has been effectively addressed with Random Forest, i.e., a popular classification algorithm that delivers a prediction by averaging the outcome of an ensemble of random decision trees. In this thesis we propose a novel algorithm based on the Random Forest framework. Our main contribution is the introduction of a new family of decision functions (aka split functions), which build up the decision trees of the random forest. Our decision functions resemble the way the human retina works, by mimicking an increase in the receptive field sizes towards the periphery of the retina. This results in a better visual acuity in the proximity of the center of view (aka fovea), which gradually degrades as we move off from the center.\\ The solution we propose improves the quality of the semantic image labelling, while preserving the low computational cost of the classical Random Forest approaches in both the training and inference phases. We conducted quantitative experiments on two standard datasets, namely eTRIMS Image Database and MSRCv2 Database, and the results we obtained are extremely encouraging.
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Linusson, Henrik. "Multi-Output Random Forests." Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-17167.

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The Random Forests ensemble predictor has proven to be well-suited for solving a multitudeof different prediction problems. In this thesis, we propose an extension to the Random Forestframework that allows Random Forests to be constructed for multi-output decision problemswith arbitrary combinations of classification and regression responses, with the goal ofincreasing predictive performance for such multi-output problems. We show that our methodfor combining decision tasks within the same decision tree reduces prediction error for mosttasks compared to single-output decision trees based on the same node impurity metrics, andprovide a comparison of different methods for combining such metrics.
Program: Magisterutbildning i informatik
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Nygren, Rasmus. "Evaluation of hyperparameter optimization methods for Random Forest classifiers." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301739.

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In order to create a machine learning model, one is often tasked with selecting certain hyperparameters which configure the behavior of the model. The performance of the model can vary greatly depending on how these hyperparameters are selected, thus making it relevant to investigate the effects of hyperparameter optimization on the classification accuracy of a machine learning model. In this study, we train and evaluate a Random Forest classifier whose hyperparameters are set to default values and compare its classification accuracy to another classifier whose hyperparameters are obtained through the use of the hyperparameter optimization (HPO) methods Random Search, Bayesian Optimization and Particle Swarm Optimization. This is done on three different datasets, and each HPO method is evaluated based on the classification accuracy change it induces across the datasets. We found that every HPO method yielded a total classification accuracy increase of approximately 2-3% across all datasets compared to the accuracies obtained using the default hyperparameters. However, due to limitations of time, data and computational resources, no assertions can be made as to whether the observed positive effect is generalizable at a larger scale. Instead, we could conclude that the utility of HPO methods is dependent on the dataset at hand.
För att skapa en maskininlärningsmodell behöver en ofta välja olika hyperparametrar som konfigurerar modellens egenskaper. Prestandan av en sådan modell beror starkt på valet av dessa hyperparametrar, varför det är relevant att undersöka hur optimering av hyperparametrar kan påverka klassifikationssäkerheten av en maskininlärningsmodell. I denna studie tränar och utvärderar vi en Random Forest-klassificerare vars hyperparametrar sätts till särskilda standardvärden och jämför denna med en klassificerare vars hyperparametrar bestäms av tre olika metoder för optimering av hyperparametrar (HPO) - Random Search, Bayesian Optimization och Particle Swarm Optimization. Detta görs på tre olika dataset, och varje HPO- metod utvärderas baserat på den ändring av klassificeringsträffsäkerhet som den medför över dessa dataset. Vi fann att varje HPO-metod resulterade i en total ökning av klassificeringsträffsäkerhet på cirka 2-3% över alla dataset jämfört med den träffsäkerhet som kruleslassificeraren fick med standardvärdena för hyperparametrana. På grund av begränsningar i form av tid och data kunde vi inte fastställa om den positiva effekten är generaliserbar till en större skala. Slutsatsen som kunde dras var istället att användbarheten av metoder för optimering av hyperparametrar är beroende på det dataset de tillämpas på.
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Lazic, Marko, and Felix Eder. "Using Random Forest model to predict image engagement rate." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229932.

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The purpose of this research is to investigate if Google Cloud Vision API combined with Random Forest Machine Learning algorithm is advanced enough in order to make a software that would evaluate how much an Instagram photo contributes to the image of a brand. The data set contains images scraped from the public Instagram feed filtered by #Nike, together with the meta data of the post. Each image was processed by the Google Cloud Vision API in order to obtain a set of descriptive labels for the content of the image. The data set was sent to the Random Forest algorithm in order to train the predictor. The results of the research shows that the predictor can only guess the correct score in about 4% of cases. The results are not very accurate, which is mostly because of the limiting factors of the Google Cloud Vision API. The conclusion that was drawn is that it is not possible to create a software that can accurately predict the engagement rate of an image with the technology that is publicly available today.
Syftet med denna forskning är att undersöka om Google Cloud Vision API kombinerat med Random Forest Machine Learning algoritmer är tillräckligt avancerade för att skapa en mjukvara som tillförlitligt kan evaluera hur mycket ett Instagram-inlägg kan bidra till bilden av ett varumärke. Datamängden innehåller bilder hämtade från Instagrams publika flöde filtrerat av #Nike, tillsammans med metadatan för inlägget. Varje bild var bearbetad av Google Cloud Vision API för att få tag på en mängd deskriptiva etiketter för innehållet av en bild. Datamängden skickades till Random Forest-algoritmen för att träna dess model. Undersökningens resultat är inte särskilt exakta, vilket främst beror på de begränsade faktorerna från Google Cloud Vision API. Slutsatsen som dras är att det inte är möjligt att tillförlitligt förutspå en bilds kvalitet med tekniken som finns allmänt tillgänglig idag.
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Asritha, Kotha Sri Lakshmi Kamakshi. "Comparing Random forest and Kriging Methods for Surrogate Modeling." Thesis, Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20230.

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The issue with conducting real experiments in design engineering is the cost factor to find an optimal design that fulfills all design requirements and constraints. An alternate method of a real experiment that is performed by engineers is computer-aided design modeling and computer-simulated experiments. These simulations are conducted to understand functional behavior and to predict possible failure modes in design concepts. However, these simulations may take minutes, hours, days to finish. In order to reduce the time consumption and simulations required for design space exploration, surrogate modeling is used. \par Replacing the original system is the motive of surrogate modeling by finding an approximation function of simulations that is quickly computed. The process of surrogate model generation includes sample selection, model generation, and model evaluation. Using surrogate models in design engineering can help reduce design cycle times and cost by enabling rapid analysis of alternative designs.\par Selecting a suitable surrogate modeling method for a given function with specific requirements is possible by comparing different surrogate modeling methods. These methods can be compared using different application problems and evaluation metrics. In this thesis, we are comparing the random forest model and kriging model based on prediction accuracy. The comparison is performed using mathematical test functions. This thesis conducted quantitative experiments to investigate the performance of methods. After experimental analysis, it is found that the kriging models have higher accuracy compared to random forests. Furthermore, the random forest models have less execution time compared to kriging for studied mathematical test problems.
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Books on the topic "Random forest"

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1948-, Eav Bov Bang, Thompson Matthew K, and Rocky Mountain Forest and Range Experiment Station (Fort Collins, Colo.), eds. Modeling initial conditions for root rot in forest stands: Random proportions. [Fort Collins, CO]: USDA Forest Service, Rocky Mountain Forest and Range Experiment Station, 1993.

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Grzeszczyk, Tadeusz. Using the Random Forest-Based Research Method for Prioritizing Project Stakeholders. 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd, 2023. http://dx.doi.org/10.4135/9781529669404.

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Shi, Feng. Learn About Random Forest in R With Data From the Adult Census Income Dataset (1996). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526495464.

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Shi, Feng. Learn About Random Forest in Python With Data From the Adult Census Income Dataset (1996). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526499363.

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1941-, Hornung Ulrich, Kotelenez P. 1943-, Papanicolaou George, and Conference on "Random Partial Differential Equations" (1989 : Mathematic Research Institute at Oberwolfach), eds. Random partial differential equations: Proceedings of the conference held at the Mathematical Research Institute at Oberwolfach, Black Forest, November 19-25, 1989. Basel: Birkhäuser Verlag, 1991.

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Genuer, Robin, and Jean-Michel Poggi. Random Forests with R. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56485-8.

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Bedi, R. S. Random thoughts: National security and current issues. New Delhi: Lancer's Books, 2006.

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Service, United States Forest. Noxious weed management project: Dakota Prairie grasslands : Billings, Slope, Golden Valley, Sioux, Grant, McHenry, McKenzie, Ransom and Richland counties in North Dakota, Corson, Perkins and Ziebach counties in South Dakota. Bismarck, ND?]: U.S. Dept. of Agriculture, Forest Service, 2007.

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Clifton, Richard. A random soldier: The words he left behind. Milford, DE: Eastwind Press, 2007.

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S, Pototzky Anthony, and Langley Research Center, eds. On the relationship between matched filter theory as applied to gust loads and phased design loads analysis. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1989.

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Book chapters on the topic "Random forest"

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Ayyadevara, V. Kishore. "Random Forest." In Pro Machine Learning Algorithms, 105–16. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3564-5_5.

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Vens, Celine. "Random Forest." In Encyclopedia of Systems Biology, 1812–13. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_612.

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Attanasi, Emil D., and Timothy C. Coburn. "Random Forest." In Encyclopedia of Mathematical Geosciences, 1182–85. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-85040-1_265.

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Schlenger, Justus. "Random Forest." In Sportinformatik, 227–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-67026-2_24.

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Truong, Dothang. "Random Forest." In Data Science and Machine Learning for Non-Programmers, 455–78. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003162872-17.

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Schlenger, Justus. "Random Forest." In Computer Science in Sport, 201–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2024. http://dx.doi.org/10.1007/978-3-662-68313-2_24.

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Žižka, Jan, František Dařena, and Arnošt Svoboda. "Random Forest." In Text Mining with Machine Learning, 193–200. First. | Boca Raton : CRC Press, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429469275-8.

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Attanasi, Emil D., and Timothy C. Coburn. "Random Forest." In Encyclopedia of Mathematical Geosciences, 1–4. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-26050-7_265-1.

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Ahlawat, Samit. "Random Forest." In Statistical Quantitative Methods in Finance, 219–39. Berkeley, CA: Apress, 2025. https://doi.org/10.1007/979-8-8688-0962-0_8.

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Suthaharan, Shan. "Random Forest Learning." In Machine Learning Models and Algorithms for Big Data Classification, 273–88. Boston, MA: Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7641-3_11.

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Conference papers on the topic "Random forest"

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Rhodes, Jake S., and Adam G. Rustad. "Random Forest-Supervised Manifold Alignment." In 2024 IEEE International Conference on Big Data (BigData), 3309–12. IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825663.

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B, Suchithra, Kalaivani T, Asha J, Sathya R, S. Ananthi, and R. Subha. "Fake Review Detectionusing Enhanced Random Forest." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS), 2157–60. IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10716987.

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Song, Bojun. "Random Forest Based Intrusion Detection System." In 2024 Asian Conference on Communication and Networks (ASIANComNet), 1–4. IEEE, 2024. https://doi.org/10.1109/asiancomnet63184.2024.10811056.

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Bicego, Manuele, and Francisco Escolano. "On learning Random Forests for Random Forest-clustering." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412014.

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"Boosted Random Forest." In International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004739005940598.

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Paul, Angshuman, and Dipti Prasad Mukherjee. "Reinforced random forest." In the Tenth Indian Conference. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/3009977.3010003.

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Bicego, Manuele. "K-Random Forests: a K-means style algorithm for Random Forest clustering." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8851820.

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Patil, Abhijit, and Sanjay Singh. "Differential private random forest." In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2014. http://dx.doi.org/10.1109/icacci.2014.6968348.

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Lee, Sangkyu, Sarah Kerns, Barry Rosenstein, Harry Ostrer, Joseph O. Deasy, and Jung Hun Oh. "Preconditioned Random Forest Regression." In BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3107411.3108201.

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Gardner, Charles, and Dan Chia-Tien Lo. "PCA Embedded Random Forest." In SoutheastCon 2021. IEEE, 2021. http://dx.doi.org/10.1109/southeastcon45413.2021.9401949.

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Reports on the topic "Random forest"

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Amrhar, A., and M. Monterial. Random Forest Optimization for Radionuclide Identification. Office of Scientific and Technical Information (OSTI), August 2020. http://dx.doi.org/10.2172/1769166.

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Chang, Ting-wei. Continuous User Authentication via Random Forest. Ames (Iowa): Iowa State University, January 2018. http://dx.doi.org/10.31274/cc-20240624-421.

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Green, Andre. Random Forest vs. Mahalanobis Ensemble and Multi-Objective LDA. Office of Scientific and Technical Information (OSTI), August 2021. http://dx.doi.org/10.2172/1818082.

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Thompson, A. A review of uncertainty evaluation methods for random forest regression. National Physical Laboratory, February 2023. http://dx.doi.org/10.47120/npl.ms41.

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Green, Andre. Navy Condition-Based Monitoring Project Update: Random Forest Impurities & Projections Overview. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1660563.

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Green, Andre. LUNA Condition-Based Monitoring Update: Random Forest and Mahalanobis Ensemble Accuracy Crossover Point. Office of Scientific and Technical Information (OSTI), September 2021. http://dx.doi.org/10.2172/1820056.

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Green, Andre. LUNA Condition Based Monitoring Update: Random Forest and Mahalanobis Ensemble Accuracy Crossover Point. Office of Scientific and Technical Information (OSTI), September 2021. http://dx.doi.org/10.2172/1822701.

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Puttanapong, Nattapong, Arturo M. Martinez Jr, Mildred Addawe, Joseph Bulan, Ron Lester Durante, and Marymell Martillan. Predicting Poverty Using Geospatial Data in Thailand. Asian Development Bank, December 2020. http://dx.doi.org/10.22617/wps200434-2.

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Abstract:
This study examines an alternative approach in estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial distribution of poverty in Thailand. It also compares the predictive performance of various econometric and machine learning methods such as generalized least squares, neural network, random forest, and support vector regression. Results suggest that intensity of night lights and other variables that approximate population density are highly associated with the proportion of population living in poverty. The random forest technique yielded the highest level of prediction accuracy among the methods considered, perhaps due to its capability to fit complex association structures even with small and medium-sized datasets.
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Labuzzetta, Charles. Spatiotemporal refinement of water classification via random forest classifiers and gap-fill imputation in LANDSAT imagery. Ames (Iowa): Iowa State University, January 2019. http://dx.doi.org/10.31274/cc-20240624-1318.

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Kanuganti, Sravya. Optimization of the Single Point Active Alignment Method (SPAAM) with a Random Forest for accurate Visual Registration. Ames (Iowa): Iowa State University, January 2019. http://dx.doi.org/10.31274/cc-20240624-1086.

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