Relevant bibliographies by topics / Random Forests

Contents

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

Academic literature on the topic 'Random Forests'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Random Forests"

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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 Random Genetic Forests (RGF), Negatively Correlated RGF (NC-RGF), and Preemptive RGF (PFS-RGF). These ensembles are compared with Breiman’s classic RF algorithm in Hadoop’s big data framework using Spark on a large, high-dimensional network intrusion dataset, UNSW-NB15.
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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, yielding Random Genetic Forests (RGF), Negatively Correlated RGF (NC-RGF), and Preemptive RGF (PFS-RGF). These ensembles are compared with Breiman’s classic RF algorithm in Hadoop’s big data framework using Spark on a large, high-dimensional network intrusion dataset, UNSW-NB15.
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Roy, Marie-Hélène, and Denis Larocque. "Prediction intervals with random forests." Statistical Methods in Medical Research 29, no. 1 (February 21, 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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Mantero, Alejandro, and Hemant Ishwaran. "Unsupervised random forests." Statistical Analysis and Data Mining: The ASA Data Science Journal 14, no. 2 (February 5, 2021): 144–67. http://dx.doi.org/10.1002/sam.11498.

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

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

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

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

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Dissertations / Theses on the topic "Random Forests"

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Gómez, Silvio Normey. "Random forests estocástico." Pontifícia Universidade Católica do Rio Grande do Sul, 2012. http://hdl.handle.net/10923/1598.

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In the Data Mining area experiments have been carried out using Ensemble Classifiers. We experimented Random Forests to evaluate the performance when randomness is applied. The results of this experiment showed us that the impact of randomness is much more relevant in Random Forests when compared with other algorithms, e. g., Bagging and Boosting. The main purpose of this work is to decrease the effect of randomness in Random Forests. To achieve the main purpose we implemented an extension of this method named Stochastic Random Forests and specified the strategy to increase the performance and stability combining the results. At the end of this work the improvements achieved are presented.
Na área de Mineração de Dados, experimentos vem sendo realizados utilizando Conjuntos de Classificadores. Estes experimentos são baseados em comparações empíricas que sofrem com a falta de cuidados no que diz respeito à questões de aleatoriedade destes métodos. Experimentamos o Random Forests para avaliar a eficiência do algoritmo quando submetido a estas questões. Estudos sobre os resultados mostram que a sensibilidade do Random Forests é significativamente maior quando comparado com a de outros métodos encontrados na literatura, como Bagging e Boosting. O proposito desta dissertação é diminuir a sensibilidade do Random Forests quando submetido a aleatoriedade. Para alcançar este objetivo, implementamos uma extensão do método, que chamamos de Random Forests Estocástico. Logo especificamos como podem ser alcançadas melhorias no problema encontrado no algoritmo combinando seus resultados. Por último, um estudo é apresentado mostrando as melhorias atingidas no problema de sensibilidade.
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Abdulsalam, Hanady. "Streaming Random Forests." Thesis, Kingston, Ont. : [s.n.], 2008. http://hdl.handle.net/1974/1321.

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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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G?mez, Silvio Normey. "Random forests estoc?stico." Pontif?cia Universidade Cat?lica do Rio Grande do Sul, 2012. http://tede2.pucrs.br/tede2/handle/tede/5226.

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In the Data Mining area experiments have been carried out using Ensemble Classifiers. We experimented Random Forests to evaluate the performance when randomness is applied. The results of this experiment showed us that the impact of randomness is much more relevant in Random Forests when compared with other algorithms, e.g., Bagging and Boosting. The main purpose of this work is to decrease the effect of randomness in Random Forests. To achieve the main purpose we implemented an extension of this method named Stochastic Random Forests and specified the strategy to increase the performance and stability combining the results. At the end of this work the improvements achieved are presented
Na ?rea de Minera??o de Dados, experimentos vem sendo realizados utilizando Conjuntos de Classificadores. Estes experimentos s?o baseados em compara??es emp?ricas que sofrem com a falta de cuidados no que diz respeito ? quest?es de aleatoriedade destes m?todos. Experimentamos o Random Forests para avaliar a efici?ncia do algoritmo quando submetido a estas quest?es. Estudos sobre os resultados mostram que a sensibilidade do Random Forests ? significativamente maior quando comparado com a de outros m?todos encontrados na literatura, como Bagging e Boosting. O proposito desta disserta??o ? diminuir a sensibilidade do Random Forests quando submetido a aleatoriedade. Para alcan?ar este objetivo, implementamos uma extens?o do m?todo, que chamamos de Random Forests Estoc?stico. Logo especificamos como podem ser alcan?adas melhorias no problema encontrado no algoritmo combinando seus resultados. Por ?ltimo, um estudo ? apresentado mostrando as melhorias atingidas no problema de sensibilidade
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Lapajne, Mikael Hellborg, and Daniel Slat. "Random Forests for CUDA GPUs." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2953.

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Context. Machine Learning is a complex and resource consuming process that requires a lot of computing power. With the constant growth of information, the need for efficient algorithms with high performance is increasing. Today's commodity graphics cards are parallel multi processors with high computing capacity at an attractive price and are usually pre-installed in new PCs. The graphics cards provide an additional resource to be used in machine learning applications. The Random Forest learning algorithm which has been showed competitive within machine learning has a good potential for performance increase through parallelization of the algorithm. Objectives. In this study we implement and review a revised Random Forest algorithm for GPU execution using CUDA. Methods. A review of previous work in the area has been done by studying articles from several sources, including Compendex, Inspec, IEEE Xplore, ACM Digital Library and Springer Link. Additional information regarding GPU architecture and implementation specific details have been obtained mainly from documentation available from Nvidia and the Nvidia developer forums. The implemented algorithm has been benchmarked and compared with two state-of-the-art CPU implementations of the Random Forest algorithm, both regarding consumed time for training and classification and for classification accuracy. Results. Measurements from benchmarks made on the three different algorithms are gathered showing the performance results of the algorithms for two publicly available data sets. Conclusion. We conclude that our implementation under the right conditions is able to outperform its competitors. We also conclude that this is only true for certain data sets depending on the size of the data sets. Moreover we conclude that there is potential for further improvements of the algorithm both regarding performance as well as adaption towards a wider range of real world applications.
Mikael: +46768539263, Daniel: +46703040693
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Diyar, Jamal. "Post-Pruning of Random Forests." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15904.

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Abstract  Context. In machine learning, ensemble methods continue to receive increased attention. Since machine learning approaches that generate a single classifier or predictor have shown limited capabilities in some contexts, ensemble methods are used to yield better predictive performance. One of the most interesting and effective ensemble algorithms that have been introduced in recent years is Random Forests. A common approach to ensure that Random Forests can achieve a high predictive accuracy is to use a large number of trees. If the predictive accuracy is to be increased with a higher number of trees, this will result in a more complex model, which may be more difficult to interpret or analyse. In addition, the generation of an increased number of trees results in higher computational power and memory requirements.  Objectives. This thesis explores automatic simplification of Random Forest models via post-pruning as a means to reduce the size of the model and increase interpretability while retaining or increasing predictive accuracy. The aim of the thesis is twofold. First, it compares and empirically evaluates a set of state-of-the-art post-pruning techniques on the simplification task. Second, it investigates the trade-off between predictive accuracy and model interpretability.  Methods. The primary research method used to conduct this study and to address the research questions is experimentation. All post-pruning techniques are implemented in Python. The Random Forest models are trained, evaluated, and validated on five selected datasets with varying characteristics.  Results. There is no significant difference in predictive performance between the compared techniques and none of the studied post-pruning techniques outperforms the other on all included datasets. The experimental results also show that model interpretability is proportional to model accuracy, at least for the studied settings. That is, a positive change in model interpretability is accompanied by a negative change in model accuracy.  Conclusions. It is possible to reduce the size of a complex Random Forest model while retaining or improving the predictive accuracy. Moreover, the suitability of a particular post-pruning technique depends on the application area and the amount of training data available. Significantly simplified models may be less accurate than the original model but tend to be perceived as more comprehensible.
Sammanfattning  Kontext. Ensemble metoder fortsätter att få mer uppmärksamhet inom maskininlärning. Då maskininlärningstekniker som genererar en enskild klassificerare eller prediktor har visat tecken på begränsad kapacitet i vissa sammanhang, har ensemble metoder vuxit fram som alternativa metoder för att åstadkomma bättre prediktiva prestanda. En av de mest intressanta och effektiva ensemble algoritmerna som har introducerats under de senaste åren är Random Forests. För att säkerställa att Random Forests uppnår en hög prediktiv noggrannhet behöver oftast ett stort antal träd användas. Resultatet av att använda ett större antal träd för att öka den prediktiva noggrannheten är en komplex modell som kan vara svår att tolka eller analysera. Problemet med det stora antalet träd ställer dessutom högre krav på såväl lagringsutrymmet som datorkraften.  Syfte. Denna uppsats utforskar möjligheten att automatiskt förenkla modeller som är genererade av Random Forests i syfte att reducera storleken på modellen, öka dess tolkningsbarhet, samt bevara eller förbättra den prediktiva noggrannheten. Syftet med denna uppsats är tvåfaldigt. Vi kommer först att jämföra och empiriskt utvärdera olika beskärningstekniker. Den andra delen av uppsatsen undersöker sambandet mellan den prediktiva noggrannheten och modellens tolkningsbarhet.  Metod. Den primära forskningsmetoden som har använts för att genomföra den studien är experiment. Alla beskärningstekniker är implementerade i Python. För att träna, utvärdera, samt validera de olika modellerna, har fem olika datamängder använts.  Resultat. Det finns inte någon signifikant skillnad i det prediktiva prestanda mellan de jämförda teknikerna och ingen av de undersökta beskärningsteknikerna är överlägsen på alla plan. Resultat från experimenten har också visat att sambandet mellan tolkningsbarhet och noggrannhet är proportionellt, i alla fall för de studerade konfigurationerna. Det vill säga, en positiv förändring i modellens tolkningsbarhet åtföljs av en negativ förändring i modellens noggrannhet.  Slutsats. Det är möjligt att reducera storleken på en komplex Random Forests modell samt bibehålla eller förbättra den prediktiva noggrannheten. Dessutom beror valet av beskärningstekniken på användningsområdet och mängden träningsdata tillgänglig. Slutligen kan modeller som är signifikant förenklade vara mindre noggranna men å andra sidan tenderar de att uppfattas som mer förståeliga.
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Xiong, Kuangnan. "Roughened Random Forests for Binary Classification." Thesis, State University of New York at Albany, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3624962.

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Binary classification plays an important role in many decision-making processes. Random forests can build a strong ensemble classifier by combining weaker classification trees that are de-correlated. The strength and correlation among individual classification trees are the key factors that contribute to the ensemble performance of random forests. We propose roughened random forests, a new set of tools which show further improvement over random forests in binary classification. Roughened random forests modify the original dataset for each classification tree and further reduce the correlation among individual classification trees. This data modification process is composed of artificially imposing missing data that are missing completely at random and subsequent missing data imputation.

Through this dissertation we aim to answer a few important questions in building roughened random forests: (1) What is the ideal rate of missing data to impose on the original dataset? (2) Should we impose missing data on both the training and testing datasets, or only on the training dataset? (3) What are the best missing data imputation methods to use in roughened random forests? (4) Do roughened random forests share the same ideal number of covariates selected at each tree node as the original random forests? (5) Can roughened random forests be used in medium- to high- dimensional datasets?

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Strobl, Carolin, Anne-Laure Boulesteix, Thomas Kneib, Thomas Augustin, and Achim Zeileis. "Conditional Variable Importance for Random Forests." BioMed Central Ltd, 2008. http://dx.doi.org/10.1186/1471-2105-9-307.

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Background Random forests are becoming increasingly popular in many scientific fields because they can cope with "small n large p" problems, complex interactions and even highly correlated predictor variables. Their variable importance measures have recently been suggested as screening tools for, e.g., gene expression studies. However, these variable importance measures show a bias towards correlated predictor variables. Results We identify two mechanisms responsible for this finding: (i) A preference for the selection of correlated predictors in the tree building process and (ii) an additional advantage for correlated predictor variables induced by the unconditional permutation scheme that is employed in the computation of the variable importance measure. Based on these considerations we develop a new, conditional permutation scheme for the computation of the variable importance measure. Conclusion The resulting conditional variable importance reflects the true impact of each predictor variable more reliably than the original marginal approach. (authors' abstract)
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Sorice, Domenico <1995&gt. "Random forests in time series analysis." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/17482.

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Machine learning algorithms are becoming more relevant in many fields from neuroscience to biostatistics, due to their adaptability and the possibility to learn from the data. In recent years, those techniques became popular in economics and found different applications in policymaking, financial forecasting, and portfolio optimization. The aim of this dissertation is two-fold. First, I will provide a review of the classification and Regression Tree and Random Forest methods proposed by [Breiman, 1984], [Breiman, 2001], then I study the effectiveness of those algorithms in time series analysis. I review the CART model and the Random Forest, which is an ensemble machine learning algorithm, based on the CART, using a variety of applications to test the performance of the algorithms. Second, I will implement an application on financial data: I will use the Random Forest algorithm to estimate a factor model based on macroeconomic variables with the aim of verifying if the Random Forest is able to capture part of the non-linear relationship between the factor considered and the index return.
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Hapfelmeier, Alexander. "Analysis of missing data with random forests." Diss., lmu, 2012. http://nbn-resolving.de/urn:nbn:de:bvb:19-150588.

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Books on the topic "Random Forests"

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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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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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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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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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Pavlov, Yu L. Random Forests. Brill Academic Publishers, 2000.

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Pavlov, Yu L. Random Forests. De Gruyter, Inc., 2019.

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Poggi, Jean-Michel, and Robin Genuer. Random Forests with R. Springer International Publishing AG, 2020.

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Book chapters on the topic "Random Forests"

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Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. "Random Forests." In The Elements of Statistical Learning, 1–18. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/b94608_15.

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Ng, Annalyn, and Kenneth Soo. "Random Forests." In Data Science – was ist das eigentlich?!, 117–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-56776-0_10.

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Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. "Random Forests." In The Elements of Statistical Learning, 587–604. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-84858-7_15.

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Genuer, Robin, and Jean-Michel Poggi. "Random Forests." In Use R!, 33–55. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56485-8_3.

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Berk, Richard A. "Random Forests." In Statistical Learning from a Regression Perspective, 205–58. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44048-4_5.

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Buhmann, M. D., Prem Melville, Vikas Sindhwani, Novi Quadrianto, Wray L. Buntine, Luís Torgo, Xinhua Zhang, et al. "Random Forests." In Encyclopedia of Machine Learning, 828. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_695.

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Williams, Graham. "Random Forests." In Data Mining with Rattle and R, 245–68. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9890-3_12.

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Singh, Pramod. "Random Forests." In Machine Learning with PySpark, 99–122. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4131-8_6.

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Hänsch, Ronny, and Olaf Hellwich. "Random Forests." In Handbuch der Geodäsie, 1–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46900-2_46-1.

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Berk, Richard A. "Random Forests." In Statistical Learning from a Regression Perspective, 233–95. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40189-4_5.

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Conference papers on the topic "Random Forests"

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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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Boström, Henrik. "Calibrating Random Forests." In 2008 Seventh International Conference on Machine Learning and Applications. IEEE, 2008. http://dx.doi.org/10.1109/icmla.2008.107.

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Chien, Chun-Han, and Hwann-Tzong Chen. "Random Decomposition Forests." In 2013 2nd IAPR Asian Conference on Pattern Recognition (ACPR). IEEE, 2013. http://dx.doi.org/10.1109/acpr.2013.97.

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Painsky, Amichai, and Saharon Rosset. "Compressing Random Forests." In 2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE, 2016. http://dx.doi.org/10.1109/icdm.2016.0148.

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Abdulsalam, Hanady, David B. Skillicorn, and Patrick Martin. "Streaming Random Forests." In 11th International Database Engineering and Applications Symposium (IDEAS 2007). IEEE, 2007. http://dx.doi.org/10.1109/ideas.2007.4318108.

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Osman, Hassab Elgawi, and Hasegawa Osamu. "Online incremental random forests." In 2007 International Conference on Machine Vision (ICMV '07). IEEE, 2007. http://dx.doi.org/10.1109/icmv.2007.4469281.

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Supinie, Timothy A., Amy McGovern, John Williams, and Jennifer Abernathy. "Spatiotemporal Relational Random Forests." In 2009 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2009. http://dx.doi.org/10.1109/icdmw.2009.89.

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Saffari, Amir, Christian Leistner, Jakob Santner, Martin Godec, and Horst Bischof. "On-line Random Forests." In 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops. IEEE, 2009. http://dx.doi.org/10.1109/iccvw.2009.5457447.

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Geremia, Ezequiel, Bjoern H. Menze, and Nicholas Ayache. "Spatially Adaptive Random Forests." In 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013). IEEE, 2013. http://dx.doi.org/10.1109/isbi.2013.6556781.

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Leistner, Christian, Amir Saffari, Jakob Santner, and Horst Bischof. "Semi-Supervised Random Forests." In 2009 IEEE 12th International Conference on Computer Vision (ICCV). IEEE, 2009. http://dx.doi.org/10.1109/iccv.2009.5459198.

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Reports on the topic "Random Forests"

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Griffin, Sean. Spatial downscaling disease risk using random forests machine learning. Engineer Research and Development Center (U.S.), February 2020. http://dx.doi.org/10.21079/11681/35618.

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Sprague, Joshua, David Kushner, James Grunden, Jamie McClain, Benjamin Grime, and Cullen Molitor. Channel Islands National Park Kelp Forest Monitoring Program: Annual report 2014. National Park Service, August 2022. http://dx.doi.org/10.36967/2293855.

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Channel Islands National Park (CHIS) has conducted long-term ecological monitoring of the kelp forests around San Miguel, Santa Rosa, Santa Cruz, Anacapa and Santa Barbara Islands since 1982. The original permanent transects were established at 16 sites between 1981 and 1986 with the first sampling beginning in 1982, this being the 33rd year of monitoring. An additional site, Miracle Mile, was established at San Miguel Island in 2001 by a commercial fisherman with assistance from the park. Miracle Mile was partially monitored from 2002 to 2004, and then fully monitored (using all KFM protocols) since 2005. In 2005, 16 additional permanent sites were established to collect baseline data from inside and adjacent to four marine reserves that were established in 2003. Sampling results from all 33 sites mentioned above are included in this report. Funding for the Kelp Forest Monitoring Program (KFM) in 2014 was provided by the National Park Service (NPS). The 2014 monitoring efforts utilized 49 days of vessel time to conduct 1,040 dives for a total of 1,059 hours of bottom time. Population dynamics of a select list of 71 “indicator species” (consisting of taxa or categories of algae, fish, and invertebrates) were measured at the 33 permanent sites. In addition, population dynamics were measured for all additional species of fish observed at the sites during the roving diver fish count. Survey techniques follow the CHIS Kelp Forest Monitoring Protocol Handbook (Davis et al. 1997) and an update to the sampling protocol handbook currently being developed (Kushner and Sprague, in progress). The techniques utilize SCUBA and surface-supplied-air to conduct the following monitoring protocols: 1 m2 quadrats, 5 m2 quadrats, band transects, random point contacts, fish transects, roving diver fish counts, video transects, size frequency measurements, and artificial recruitment modules. Hourly temperature data were collected using remote temperature loggers at 32 sites, the exception being Miracle Mile where there is no temperature logger installed. This annual report contains a brief description of each site including any notable observations or anomalies, a summary of methods used, and monitoring results for 2014. All the data collected during 2014 can be found in the appendices and in an Excel workbook on the NPS Integrated Resource Management Applications (IRMA) portal. In the 2013 annual report (Sprague et al. 2020) several changes were made to the appendices. Previously, annual report density and percent cover data tables only included the current year’s data. Now, density and percent cover data are presented in graphical format and include all years of available monitoring data. Roving diver fish count (RDFC), fish size frequency, natural habitat size frequency, and Artificial Recruitment Module (ARM) size frequency data are now stored on IRMA at https://irma.nps.gov/DataStore/Reference/Profile/2259651. The temperature data graphs in Appendix L include the same graphs that were used in past reports, but include additional violin plot sections that compare monthly means from the current year to past years. In addition to the changes listed above, the layout of the discussion section was reordered by species instead of by site. The status of kelp forests differed among the five park islands. This is a result of a combination of factors including but not limited to, oceanography, biogeography and associated differences in species abundance and composition, as well as sport and commercial fishing pressure. All 33 permanent sites were established in areas that had or were historically known to have had kelp forests in the past. In 2014, 15 of the 33 sites monitored were characterized as developing kelp forest, kelp forest or mature kelp forest. In addition, three sites were in a state of transition. Two sites were part kelp forest and part dominated by Strongylocentrotus purpuratus...
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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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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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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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Lunsford, Kurt G., and Kenneth D. West. Random Walk Forecasts of Stationary Processes Have Low Bias. Federal Reserve Bank of Cleveland, August 2023. http://dx.doi.org/10.26509/frbc-wp-202318.

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We study the use of a zero mean first difference model to forecast the level of a scalar time series that is stationary in levels. Let bias be the average value of a series of forecast errors. Then the bias of forecasts from a misspecified ARMA model for the first difference of the series will tend to be smaller in magnitude than the bias of forecasts from a correctly specified model for the level of the series. Formally, let P be the number of forecasts. Then the bias from the first difference model has expectation zero and a variance that is O(1/P²), while the variance of the bias from the levels model is generally O(1/P). With a driftless random walk as our first difference model, we confirm this theoretical result with simulations and empirical work: random walk bias is generally one-tenth to one-half that of an appropriately specified model fit to levels.
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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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Schoening, Timm. PyQuickMaps. GEOMAR, 2021. http://dx.doi.org/10.3289/sw_4_2021.

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A slim python library to link maps and sampling data with prediction methods. PyQuickMaps can do interpolation (with scipy.interpolate.griddata), kriging (with pykrige) and random forest regression (with sklearn.ensemble.RandomForestRegressor). It also features plotting nice geographical maps with matplotlib and storing those to geotiff with rasterio. Coordinate transforms are managed internally with osgeo/gdal.
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Rossi, Jose Luiz, Carlos Piccioni, Marina Rossi, and Daniel Cuajeiro. Brazilian Exchange Rate Forecasting in High Frequency. Inter-American Development Bank, September 2022. http://dx.doi.org/10.18235/0004488.

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We investigated the predictability of the Brazilian exchange rate at High Frequency (1, 5 and 15 minutes), using local and global economic variables as predictors. In addition to the Linear Regression method, we use Machine Learning algorithms such as Ridge, Lasso, Elastic Net, Random Forest and Gradient Boosting. When considering contemporary predictors, it is possible to outperform the Random Walk at all frequencies, with local economic variables having greater predictive power than global ones. Machine Learning methods are also capable of reducing the mean squared error. When we consider only lagged predictors, it is possible to beat the Random Walk if we also consider the Brazilian Real futures as an additional predictor, for the frequency of one minute and up to two minutes ahead, confirming the importance of the Brazilian futures market in determining the spot exchange rate.
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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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