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Dissertations / Theses on the topic 'And random forests'

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Published: 3 June 2025

Last updated: 31 July 2025

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1

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'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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Wonkye, Yaa Tawiah. "Innovations of random forests for longitudinal data." Bowling Green State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1563054152739397.

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Auret, Lidia. "Process monitoring and fault diagnosis using random forests." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/5360.

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Thesis (PhD (Process Engineering))--University of Stellenbosch, 2010.<br>Dissertation presented for the Degree of DOCTOR OF PHILOSOPHY (Extractive Metallurgical Engineering) in the Department of Process Engineering at the University of Stellenbosch<br>ENGLISH ABSTRACT: Fault diagnosis is an important component of process monitoring, relevant in the greater context of developing safer, cleaner and more cost efficient processes. Data-driven unsupervised (or feature extractive) approaches to fault diagnosis exploit the many measurements available on modern plants. Certain current unsupervi

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Fawagreh, Khaled. "On pruning and feature engineering in Random Forests." Thesis, Robert Gordon University, 2016. http://hdl.handle.net/10059/2113.

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Random Forest (RF) is an ensemble classification technique that was developed by Leo Breiman over a decade ago. Compared with other ensemble techniques, it has proved its accuracy and superiority. Many researchers, however, believe that there is still room for optimizing RF further by enhancing and improving its performance accuracy. This explains why there have been many extensions of RF where each extension employed a variety of techniques and strategies to improve certain aspect(s) of RF. The main focus of this dissertation is to develop new extensions of RF using new optimization technique

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Merrill, Andrew C. "Investigations of Variable Importance Measures Within Random Forests." DigitalCommons@USU, 2009. https://digitalcommons.usu.edu/etd/7078.

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Random Forests (RF) (Breiman 2001; Breiman and Cutler 2004) is a completely nonparametric statistical learning procedure that may be used for regression analysis and. A feature of RF that is drawing a lot of attention is the novel algorithm that is used to evaluate the relative importance of the predictor/explanatory variables. Other machine learning algorithms for regression and classification, such as support vector machines and artificial neural networks (Hastie et al. 2009), exhibit high predictive accuracy but provide little insight into predictive power of individual variables. In contra

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Quach, Anna. "Extensions and Improvements to Random Forests for Classification." DigitalCommons@USU, 2017. https://digitalcommons.usu.edu/etd/6755.

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The motivation of my dissertation is to improve two weaknesses of Random Forests. One, the failure to detect genetic interactions between two single nucleotide polymorphisms (SNPs) in higher dimensions when the interacting SNPs both have weak main effects and two, the difficulty of interpretation in comparison to parametric methods such as logistic regression, linear discriminant analysis, and linear regression. We focus on detecting pairwise SNP interactions in genome case-control studies. We determine the best parameter settings to optimize the detection of SNP interactions and improve the e

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Parfionovas, Andrejus. "Enhancement of Random Forests Using Trees with Oblique Splits." DigitalCommons@USU, 2013. http://digitalcommons.usu.edu/etd/1508.

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This work presents an enhancement to the classification tree algorithm which forms the basis for Random Forests. Differently from the classical tree-based methods that focus on one variable at a time to separate the observations, the new algorithm performs the search for the best split in two-dimensional space using a linear combination of variables. Besides the classification, the method can be used to determine variables interaction and perform feature extraction. Theoretical investigations and numerical simulations were used to analyze the properties and performance of the new approach. Com

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Tang, Ying. "Real-time automatic face tracking using adaptive random forests." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=95172.

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Tracking is treated as a pixel-based binary classification problem in this thesis. An ensemble strong classifier obtained as a weighted combination of several random forests (weak classifiers), is trained on pixel feature vectors. The strong classifier is then used to classify the pixels belonging to the face or the background in the next frame. The classification margins are used to create a confidence map, whose peak indicates the new location of the face. The peak is located by Camshift which adjusts the size of the tracked face. The random forests in the ensemble are updated using AdaBoost

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Michaelson, Jacob. "Applications and extensions of Random Forests in genetic and environmental studies." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-64099.

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Transcriptional regulation refers to the molecular systems that control the concentration of mRNA species within the cell. Variation in these controlling systems is not only responsible for many diseases, but also contributes to the vast phenotypic diversity in the biological world. There are powerful experimental approaches to probe these regulatory systems, and the focus of my doctoral research has been to develop and apply effective computational methods that exploit these rich data sets more completely. First, I present a method for mapping genetic regulators of gene expression (expression

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Sandsveden, Daniel. "Evaluation of Random Forests for Detection and Localization of Cattle Eyes." Thesis, Linköpings universitet, Datorseende, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-121540.

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In a time when cattle herds grow continually larger the need for automatic methods to detect diseases is ever increasing. One possible method to discover diseases is to use thermal images and automatic head and eye detectors. In this thesis an eye detector and a head detector is implemented using the Random Forests classifier. During the implementation the classifier is evaluated using three different descriptors: Histogram of Oriented Gradients, Local Binary Patterns, and a descriptor based on pixel differences. An alternative classifier, the Support Vector Machine, is also evaluated for comp

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Reiter, Richard M. "Prediction of recurrence in thin melanoma using trees and random forests /." Electronic version (PDF), 2005. http://dl.uncw.edu/etd/2005/reiterr/richardreiter.html.

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Hansson, Kim, and Erik Hörlin. "Active learning via Transduction in Regression Forests." Thesis, Blekinge Tekniska Högskola, Institutionen för kreativa teknologier, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-10935.

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Context. The amount of training data required to build accurate modelsis a common problem in machine learning. Active learning is a techniquethat tries to reduce the amount of required training data by making activechoices of which training data holds the greatest value.Objectives. This thesis aims to design, implement and evaluate the Ran-dom Forests algorithm combined with active learning that is suitable forpredictive tasks with real-value data outcomes where the amount of train-ing data is small. machine learning algorithms traditionally requires largeamounts of training data to create a g

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Hapfelmeier, Alexander [Verfasser], and Kurt [Akademischer Betreuer] Ulm. "Analysis of missing data with random forests / Alexander Hapfelmeier. Betreuer: Kurt Ulm." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2012. http://d-nb.info/102904032X/34.

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Matheson, David. "An empirical study of practical, theoretical and online variants of random forests." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/46586.

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Random forests are ensembles of randomized decision trees where diversity is created by injecting randomness into the fitting of each tree. The combination of their accuracy and their simplicity has resulted in their adoption in many applications. Different variants have been developed with different goals in mind: improving predictive accuracy, extending the range of application to online and structure domains, and introducing simplifications for theoretical amenability. While there are many subtle differences among the variants, the core difference is the method of selecting candidate split

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Adriansson, Nils, and Ingrid Mattsson. "Forecasting GDP Growth, or How Can Random Forests Improve Predictions in Economics?" Thesis, Uppsala universitet, Statistiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-243028.

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GDP is used to measure the economic state of a country and accurate forecasts of it is therefore important. Using the Economic Tendency Survey we investigate forecasting quarterly GDP growth using the data mining technique Random Forest. Comparisons are made with a benchmark AR(1) and an ad hoc linear model built on the most important variables suggested by the Random Forest. Evaluation by forecasting shows that the Random Forest makes the most accurate forecast supporting the theory that there are benefits to using Random Forests on economic time series.

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Mohammed, D. Y. "Overlapped speech and music segmentation using singular spectrum analysis and random forests." Thesis, University of Salford, 2017. http://usir.salford.ac.uk/43773/.

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Recent years have seen ever-increasing volumes of digital media archives and an enormous amount of user-contributed content. As demand for indexing and searching these resources has increased, and new technologies such as multimedia content management systems, en-hanced digital broadcasting, and semantic web have emerged, audio information mining and automated metadata generation have received much attention. Manual indexing and metadata tagging are time-consuming and subject to the biases of individual workers. An automated architecture able to extract information from audio signals, generate

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Samarakoon, Prasad. "Random Regression Forests for Fully Automatic Multi-Organ Localization in CT Images." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAM039/document.

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La localisation d'un organe dans une image médicale en délimitant cet organe spécifique par rapport à une entité telle qu'une boite ou sphère englobante est appelée localisation d'organes. La localisation multi-organes a lieu lorsque plusieurs organes sont localisés simultanément. La localisation d'organes est l'une des étapes les plus cruciales qui est impliquée dans toutes les phases du traitement du patient à partir de la phase de diagnostic à la phase finale de suivi. L'utilisation de la technique d'apprentissage supervisé appelée forêts aléatoires (Random Forests) a montré des résultats t

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Stum, Alexander Knell. "Random Forests Applied as a Soil Spatial Predictive Model in Arid Utah." DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/736.

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Initial soil surveys are incomplete for large tracts of public land in the western USA. Digital soil mapping offers a quantitative approach as an alternative to traditional soil mapping. I sought to predict soil classes across an arid to semiarid watershed of western Utah by applying random forests (RF) and using environmental covariates derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and digital elevation models (DEM). Random forests are similar to classification and regression trees (CART). However, RF is doubly random. Many (e.g., 500) weak trees are grown (trained) independentl

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edu, rdlyons@indiana. "Markov Chain Intersections and the Loop--Erased Walk." ESI preprints, 2001. ftp://ftp.esi.ac.at/pub/Preprints/esi1058.ps.

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Hudec, Vladimír. "Klasifikační metody pro data z mikročipů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-236982.

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This paper discusses about the data obtained from gene chips and methods of their analysis. Analyzes some methods for analyzing these data and focus on the method of "Random Forests". Shows dataset that is used for specific experiments. Methods are realized in R language environment. Than they are tested, and the results are presented and compared. Results with method "Random Forests" are compared with other experiments on same dataset.

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Li, Ke. "Customer Relationship Management: from Conversion to Churn to Winback." Diss., Temple University Libraries, 2013. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/221333.

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Business Administration/Marketing<br>Ph.D.<br>With the grant of a big CRM dataset from a large media company, this dissertation examines four different categories of factors that could impact three stages of customer relationship management, namely customer acquisition, retention, and winback of lost customers. Specifically, with the aid of machine learning method of random forests and text mining technique, this study identify among the factors of customer heterogeneity (e.g. in usage of self-care service channels, duration of service, responsiveness to marketing actions), firm's marketing in

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Hjerpe, Adam. "Computing Random Forests Variable Importance Measures (VIM) on Mixed Numerical and Categorical Data." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-185496.

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The Random Forest model is commonly used as a predictor function and the model have been proven useful in a variety of applications. Their popularity stems from the combination of providing high prediction accuracy, their ability to model high dimensional complex data, and their applicability under predictor correlations. This report investigates the random forest variable importance measure (VIM) as a means to find a ranking of important variables. The robustness of the VIM under imputation of categorical noise, and the capability to differentiate informative predictors from non-informative v

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Persson, Karl. "Predicting movie ratings : A comparative study on random forests and support vector machines." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-11119.

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The aim of this work is to evaluate the prediction performance of random forests in comparison to support vector machines, for predicting the numerical user ratings of a movie using pre-release attributes such as its cast, directors, budget and movie genres. In order to answer this question an experiment was conducted on predicting the overall user rating of 3376 hollywood movies, using data from the well established movie database IMDb. The prediction performance of the two algorithms was assessed and compared over three commonly used performance and error metrics, as well as evaluated by the

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Pauly, Olivier Verfasser], Nassir [Akademischer Betreuer] [Navab, and Nicholas [Akademischer Betreuer] Ayache. "Random Forests for Medical Applications / Olivier Pauly. Gutachter: Nicholas Ayache. Betreuer: Nassir Navab." München : Universitätsbibliothek der TU München, 2012. http://d-nb.info/1030099510/34.

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Kimes, Ryan Vincent. "Quantifying the Effects of Correlated Covariates on Variable Importance Estimates from Random Forests." VCU Scholars Compass, 2006. http://scholarscompass.vcu.edu/etd/1433.

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Recent advances in computing technology have lead to the development of algorithmic modeling techniques. These methods can be used to analyze data which are difficult to analyze using traditional statistical models. This study examined the effectiveness of variable importance estimates from the random forest algorithm in identifying the true predictor among a large number of candidate predictors. A simulation study was conducted using twenty different levels of association among the independent variables and seven different levels of association between the true predictor and the response. We

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Varatharajah, Thujeepan, and Eriksson Victor. "A comparative study on artificial neural networks and random forests for stock market prediction." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186452.

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This study investigates the predictive performance of two different machine learning (ML) models on the stock market and compare the results. The chosen models are based on artificial neural networks (ANN) and random forests (RF). The models are trained on two separate data sets and the predictions are made on the next day closing price. The input vectors of the models consist of 6 different financial indicators which are based on the closing prices of the past 5, 10 and 20 days. The performance evaluation are done by analyzing and comparing such values as the root mean squared error (RMSE) an

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Petersson, Andreas. "Data mining file sharing metadata : A comparison between Random Forests Classificiation and Bayesian Networks." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-11180.

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In this comparative study based on experimentation it is demonstrated that the two evaluated machine learning techniques, Bayesian networks and random forests, have similar predictive power in the domain of classifying torrents on BitTorrent file sharing networks. This work was performed in two steps. First, a literature analysis was performed to gain insight into how the two techniques work and what types of attacks exist against BitTorrent file sharing networks. After the literature analysis, an experiment was performed to evaluate the accuracy of the two techniques. The results show no sign

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Petersson, Andreas. "Data mining file sharing metadata : A comparison between Random Forests Classification and Bayesian Networks." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-11285.

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In this comparative study based on experimentation it is demonstrated that the two evaluated machine learning techniques, Bayesian networks and random forests, have similar predictive power in the domain of classifying torrents on BitTorrent file sharing networks.This work was performed in two steps. First, a literature analysis was performed to gain insight into how the two techniques work and what types of attacks exist against BitTorrent file sharing networks. After the literature analysis, an experiment was performed to evaluate the accuracy of the two techniques.The results show no signif

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Kanavati, Fahdi. "Efficient extraction of semantic information from medical images in large datasets using random forests." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/58017.

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Large datasets of unlabelled medical images are increasingly becoming available; however only a small subset tend to be manually semantically labelled as it is a tedious and extremely time-consuming task to do for large datasets. This thesis aims to tackle the problem of efficiently extracting semantic information in the form of image segmentations and organ localisations from large datasets of unlabelled medical images. To do so, we investigate the suitability of supervoxels and random classification forests for the task. The first contribution of this thesis is a novel method for efficiently

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Pasquale, Daniel L. (Daniel Louis). "Characterizing drag and velocity within model mangrove forests of ordered and random tree arrangement." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111525.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (page 50).<br>Changes in velocity and drag force on model mangrove trees within 13 different simulated mangrove forest segments in a flume were investigated. The simulated forests were composed of 1/12 scale model Rhizophora mangrove trees placed at three densities: low (3.42 trees/m²), medium (6.34 trees/m²), and high (9.27 trees/m²). For the low tree density cases, one forest with ordered tree placemen

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Julock, Gregory Alan. "The Effectiveness of a Random Forests Model in Detecting Network-Based Buffer Overflow Attacks." NSUWorks, 2013. http://nsuworks.nova.edu/gscis_etd/190.

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Buffer Overflows are a common type of network intrusion attack that continue to plague the networked community. Unfortunately, this type of attack is not well detected with current data mining algorithms. This research investigated the use of Random Forests, an ensemble technique that creates multiple decision trees, and then votes for the best tree. The research Investigated Random Forests' effectiveness in detecting buffer overflows compared to other data mining methods such as CART and Naïve Bayes. Random Forests was used for variable reduction, cost sensitive classification was applied, an

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Herlitz, Mattias. "Analyzing the Tobii Real-world-mapping tool and improving its workflow using Random Forests." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-228474.

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The Tobii Pro Glasses 2 are used to record gaze data that is used for market research or scientific experiments. To make extraction of relevant statistics more efficient, the gaze points in the recorded video are mapped to a static snapshot with areas of interests (AOIs). The most important statistics revolve around fixations. A fixation is when a person is keeping his or her vision still for a short period of time. The method most used today is to manually map the gaze points. However, a faster method is automated mapping using the Real World Mapping (RWM) tool. In order to examine the reliab

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Brokamp, Richard C. "Land Use Random Forests for Estimation of Exposure to Elemental Components of Particulate Matter." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1463130851.

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Williams, Paige T. "Mapping Smallholder Forest Plantations in Andhra Pradesh, India using Multitemporal Harmonized Landsat Sentinel-2 S10 Data." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/104234.

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The objective of this study was to develop a method by which smallholder forest plantations can be mapped accurately in Andhra Pradesh, India using multitemporal (intra- and inter-annual) visible and near-infrared (VNIR) bands from the Sentinel-2 MultiSpectral Instruments (MSIs). Dependency on and scarcity of wood products have driven the deforestation and degradation of natural forests in Southeast Asia. At the same time, forest plantations have been established both within and outside of forests, with the latter (as contiguous blocks) being the focus of this study. The ecosystem services pro

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ARAÚJO, Gilderlanio Santana de. "Uso de random forests e redes biológicas na associação de poliformismos à doença de Alzheimer." Universidade Federal de Pernambuco, 2013. https://repositorio.ufpe.br/handle/123456789/18012.

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Submitted by Irene Nascimento (irene.kessia@ufpe.br) on 2016-10-18T19:17:10Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao -Gilderlanio Santana de Araujo.pdf: 9533988 bytes, checksum: 951b1cf090729a87ebf3a8741ff00ad4 (MD5)<br>Made available in DSpace on 2016-10-18T19:17:10Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao -Gilderlanio Santana de Araujo.pdf: 9533988 bytes, checksum: 951b1cf090729a87ebf3a8741ff00ad4 (MD5) Previous issue date: 2013-03-07<br>FACEPE<

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Strobl, Carolin, Anne-Laure Boulesteix, Achim Zeileis, and Torsten Hothorn. "Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2006. http://epub.wu.ac.at/1274/1/document.pdf.

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Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers relevant for the prediction of a certain disease. We show that random forest variable importance measures are a sensible means for variable selection in many applications, but are not reliable in situations where potential predictor variables vary in their scale level or their number of categories. This is particularly important in genomics and com

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Geremia, Ezequiel. "Spatial random forests for brain lesions segmentation in MRIs and model-based tumor cell extrapolation." Phd thesis, Université Nice Sophia Antipolis, 2013. http://tel.archives-ouvertes.fr/tel-00838795.

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The large size of the datasets produced by medical imaging protocols contributes to the success of supervised discriminative methods for semantic labelling of images. Our study makes use of a general and efficient emerging framework, discriminative random forests, for the detection of brain lesions in multi-modal magnetic resonance images (MRIs). The contribution is three-fold. First, we focus on segmentation of brain lesions which is an essential task to diagnosis, prognosis and therapy planning. A context-aware random forest is designed for the automatic multi-class segmentation of MS lesion

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Bylund, Rebecca, and Höök Malin J-son. "Går det prediktera demens? : En jämförande studie mellan Logistisk regression, Elastic Net och Random Forests." Thesis, Umeå universitet, Statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-149728.

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Denna studie tar avstamp i ett tidigare resultat av Boraxbekk et al. (2015) som genom data från Betula-projektet visat att vissa episodiska minnestester tillsammans med ålder ochutbildningsnivå har signifikanta samband med utvecklandet av demenssjukdomar. Syftet med denna studie är att jämföra klassificeringsmetoderna Random Forests, Elastic Net ochLogistisk Regression med avseende på prestationer vid klassificering av demens. I studien undersöks förutom det binära fallet (demens: ja/nej) prediktionsprestationer för utveckling av demens inom tidsspannen 1-10 år och 11-22 år. Detta för att unde

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Al, Maathidi M. M. "Optimal feature selection and machine learning for high-level audio classification : a random forests approach." Thesis, University of Salford, 2017. http://usir.salford.ac.uk/44338/.

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Content related information, metadata, and semantics can be extracted from soundtracks of multimedia files. Speech recognition, music information retrieval and environmental sound detection techniques have been developed into a fairly mature technology enabling a final text mining process to obtain semantics for the audio scene. An efficient speech, music and environmental sound classification system, which correctly identify these three types of audio signals and feed them into dedicated recognisers, is a critical pre-processing stage for such a content analysis system. The performance and co

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Aichele, Figueroa Diego Andrés. "Detección de anomalías en componentes mecánicos en base a Deep Learning y Random Cut Forests." Tesis, Universidad de Chile, 2019. http://repositorio.uchile.cl/handle/2250/170571.

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Memoria para optar al título de Ingeniero Civil Mecánico<br>Dentro del área de mantenimiento, el monitorear un equipo puede ser de gran utilidad ya que permite advertir cualquier anomalía en el funcionamiento interno de éste, y así, se puede corregir cualquier desperfecto antes de que se produzca una falla de mayor gravedad. En data mining, detección de anomalías es el ejercicio de identificar elementos anómalos, es decir, aquellos elementos que difieren a lo común dentro de un set de datos. Detección de anomalías tiene aplicación en diferentes dominios, por ejemplo, hoy en día se utiliza en

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Goodwin, Christopher C. H. "The Influence of Cost-sharing Programs on Southern Non-industrial Private Forests." Thesis, Virginia Tech, 2001. http://hdl.handle.net/10919/30895.

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This study was undertaken in response to concerns that the decreasing levels of funding for government tree planting cost share programs will result in significant reductions in non-industrial private tree planting efforts in the South. The purpose of this study is to quantify how the funding of various cost share programs, and market signals interact and affect the level of private tree planting. The results indicate that the ACP, CRP, and Soil Bank programs have been more influential than the FIP, FRM, FSP, SIP, and State run subsidy programs. Reductions in the CRP funding will result in

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