Relevant bibliographies by topics / And random forests

Contents

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

Academic literature on the topic 'And random forests'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "And 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 R
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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, yield
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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 (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 (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 (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 (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 (2011): 1974–75. http://dx.doi.org/10.1097/jto.0b013e318233d835.

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

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Dissertations / Theses on the topic "And 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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Made available in DSpace on 2013-08-07T18:43:07Z (GMT). No. of bitstreams: 1 000449231-Texto+Completo-0.pdf: 1860025 bytes, checksum: 1ace09799e27fa64938e802d2d91d1af (MD5) Previous issue date: 2012<br>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 t
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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 th
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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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Made available in DSpace on 2015-04-14T14:50:03Z (GMT). No. of bitstreams: 1 449231.pdf: 1860025 bytes, checksum: 1ace09799e27fa64938e802d2d91d1af (MD5) Previous issue date: 2012-08-31<br>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 ra
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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&apos;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 p
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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
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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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<p> 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 correlat
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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 addi
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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
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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 "And random forests"

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Genuer, Robin, and Jean-Michel Poggi. Random Forests with R. 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. 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. 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. 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). 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). SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526499363.

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Bessonov, Aleksey. The study of criminal activity using artificial intelligence. INFRA-M Academic Publishing LLC., 2025. https://doi.org/10.12737/2195488.

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The monograph describes the technology of building digital crime models, including the preparation of data on criminal acts for study using mathematical statistics and artificial intelligence methods, the features of studying such data through various artificial intelligence methods, including neural networks, gradient boosting, decision trees, random forest, clustering, etc. Special attention is paid to the use of mathematical statistics and artificial intelligence methods in the study of serial crimes in science and practice. It is intended for scientists and practitioners of law enforcement
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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. 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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Book chapters on the topic "And random forests"

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Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. "Random Forests." In The Elements of Statistical Learning. 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?! 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. 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! 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. 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, et al. "Random Forests." In Encyclopedia of Machine Learning. 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. 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. 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. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46900-2_46-1.

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Schonlau, Matthias. "Random Forests." In Applied Statistical Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-33390-3_10.

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Conference papers on the topic "And random forests"

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Akbarian, Fatemeh, and Amir Aminifar. "Membership Inference Attack in Random Forests." In ESANN 2025. Ciaco - i6doc.com, 2025. https://doi.org/10.14428/esann/2025.es2025-184.

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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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Reports on the topic "And random forests"

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Goode, Katherine, and James Tucker. FORESTR: Finding, Organizing, Representing, Explaining, Summarizing, and Thinning Random forests. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2472741.

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

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Han, Shangxuan. Stock Prediction with Random Forests and Long Short-term Memory. Iowa State University, 2019. http://dx.doi.org/10.31274/cc-20240624-1334.

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Johnson, Madeline. Handwriting Identification using Random Forests and Score-based Likelihood Ratios. Iowa State University, 2021. http://dx.doi.org/10.31274/cc-20240624-810.

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Roldán-Ferrín, Felipe, and Julián A. Parra-Polania. ENHANCING INFLATION NOWCASTING WITH ONLINE SEARCH DATA: A RANDOM FOREST APPLICATION FOR COLOMBIA. Banco de la República, 2025. https://doi.org/10.32468/be.1318.

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This paper evaluates the predictive capacity of a machine learning model based on Random Forests (RF), combined with Google Trends (GT) data, for nowcasting monthly inflation in Colombia. The proposed RF-GT model is trained using historical inflation data, macroeconomic indicators, and internet search activity. After optimizing the model’s hyperparameters through time series cross-validation, we assess its out-of-sample performance over the period 2023–2024. The results are benchmarked against traditional approaches, including SARIMA, Ridge, and Lasso regressions, as well as professional forec
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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, 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
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Amrhar, A., and M. Monterial. Random Forest Optimization for Radionuclide Identification. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1769166.

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

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

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