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Dissertations / Theses on the topic 'Random forest'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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

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In silico experimentation is the process of using computational and statistical models to predict medicinal properties in chemicals; as a means of reducing lab work and increasing success rate this process has become an important part of modern drug development. There are various ways of representing molecules - the problem that motivated this paper derives from collecting substructures of the chemical into what is known as fractional representations. Assembling large sets of molecules represented in this way will result in sparse data, where a large portion of the set is null values. This con

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Karlsson, Isak. "Order in the random forest." Doctoral thesis, Stockholms universitet, Institutionen för data- och systemvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-142052.

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In many domains, repeated measurements are systematically collected to obtain the characteristics of objects or situations that evolve over time or other logical orderings. Although the classification of such data series shares many similarities with traditional multidimensional classification, inducing accurate machine learning models using traditional algorithms are typically infeasible since the order of the values must be considered. In this thesis, the challenges related to inducing predictive models from data series using a class of algorithms known as random forests are studied for the

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Siegel, Kathryn I. (Kathryn Iris). "Incremental random forest classifiers in spark." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106105.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (page 53).<br>The random forest is a machine learning algorithm that has gained popularity due to its resistance to noise, good performance, and training efficiency. Random forests are typically constructed using a static dataset; to accommodate new data, random forests are usually regrown. This thesis presents two main strategies for updating random forests incrementally, rather than entirely re

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4

Cheng, Chuan. "Random forest training on reconfigurable hardware." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/28122.

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Random Forest (RF) is one of the most widely used supervised learning methods available. An RF is ensemble of decision tree classifiers with injection of several sources of randomness. It demonstrates a set of improvement over single decision and regression trees and is comparable or superior to major classification tools such as support vector machine (SVM) and adaptive boosting (Adaboost) with respect to accuracy, interpretability, robustness and processing speed. RF can be generally divided into training process and predicting process. Recently with emergence of large-scale data mining appl

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Nelson, Marc. "Evaluating Multitemporal Sentinel-2 data for Forest Mapping using Random Forest." Thesis, Stockholms universitet, Institutionen för naturgeografi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-146657.

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The mapping of land cover using remotely sensed data is most effective when a robust classification method is employed. Random forest is a modern machine learning algorithm that has recently gained interest in the field of remote sensing due to its non-parametric nature, which may be better suited to handle complex, high-dimensional data than conventional techniques. In this study, the random forest method is applied to remote sensing data from the European Space Agency’s new Sentinel-2 satellite program, which was launched in 2015 yet remains relatively untested in scientific literature using

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Lak, Kameran Majeed Mohammed <1985&gt. "Retina-inspired random forest for semantic image labelling." Master's Degree Thesis, Università Ca' Foscari Venezia, 2015. http://hdl.handle.net/10579/5970.

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One of the most challenging problem in computer vision community is semantic image labeling, which requires assigning a semantic class to each pixel in an image. In the literature, this problem has been effectively addressed with Random Forest, i.e., a popular classification algorithm that delivers a prediction by averaging the outcome of an ensemble of random decision trees. In this thesis we propose a novel algorithm based on the Random Forest framework. Our main contribution is the introduction of a new family of decision functions (aka split functions), which build up the decision trees of

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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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Nygren, Rasmus. "Evaluation of hyperparameter optimization methods for Random Forest classifiers." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301739.

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In order to create a machine learning model, one is often tasked with selecting certain hyperparameters which configure the behavior of the model. The performance of the model can vary greatly depending on how these hyperparameters are selected, thus making it relevant to investigate the effects of hyperparameter optimization on the classification accuracy of a machine learning model. In this study, we train and evaluate a Random Forest classifier whose hyperparameters are set to default values and compare its classification accuracy to another classifier whose hyperparameters are obtained thr

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Lazic, Marko, and Felix Eder. "Using Random Forest model to predict image engagement rate." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229932.

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The purpose of this research is to investigate if Google Cloud Vision API combined with Random Forest Machine Learning algorithm is advanced enough in order to make a software that would evaluate how much an Instagram photo contributes to the image of a brand. The data set contains images scraped from the public Instagram feed filtered by #Nike, together with the meta data of the post. Each image was processed by the Google Cloud Vision API in order to obtain a set of descriptive labels for the content of the image. The data set was sent to the Random Forest algorithm in order to train the pre

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Asritha, Kotha Sri Lakshmi Kamakshi. "Comparing Random forest and Kriging Methods for Surrogate Modeling." Thesis, Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20230.

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The issue with conducting real experiments in design engineering is the cost factor to find an optimal design that fulfills all design requirements and constraints. An alternate method of a real experiment that is performed by engineers is computer-aided design modeling and computer-simulated experiments. These simulations are conducted to understand functional behavior and to predict possible failure modes in design concepts. However, these simulations may take minutes, hours, days to finish. In order to reduce the time consumption and simulations required for design space exploration, surrog

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Williams, Alyssa. "Hybrid Recommender Systems via Spectral Learning and a Random Forest." Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/etd/3666.

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We demonstrate spectral learning can be combined with a random forest classifier to produce a hybrid recommender system capable of incorporating meta information. Spectral learning is supervised learning in which data is in the form of one or more networks. Responses are predicted from features obtained from the eigenvector decomposition of matrix representations of the networks. Spectral learning is based on the highest weight eigenvectors of natural Markov chain representations. A random forest is an ensemble technique for supervised learning whose internal predictive model can be interprete

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Elfving, Jan, and Sebastian Kalucza. "Random Forest för överlevnadsanalys med konkurrerande utfall : Prediktion av demens." Thesis, Umeå universitet, Statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184927.

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Statistik som ämnesområde är i ständig utveckling. I takt med att datorers beräkningskapacitet stadigt förbättrats har mer beräkningsintensiva metoder som tidigare varit krångliga att tillämpa nu blivit lättillgängliga. Random Forest är ett exempel på en sådan metod som vuxit fram ur dessa premisser och visat sig fungera väl på en rad statistiska problem, prediktionsproblem inkluderat. En sådan problemtyp är s.k. överlevnadsanalys. Ett sätt att göra överlevnadsmodellen mer verklighetsnära är att utöka den till att även beakta konkurrerande händelser. Konkurrerande händelser är händelser som tä

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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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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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SILVA, J. P. M. "PROGNOSE DA PRODUÇÃO FLORESTAL UTILIZANDO SISTEMA NEURO-FUZZY E RANDOM FOREST." Universidade Federal do Espírito Santo, 2018. http://repositorio.ufes.br/handle/10/7680.

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Made available in DSpace on 2018-08-01T22:35:53Z (GMT). No. of bitstreams: 1 tese_11765_Dissertação JEFERSON 2018-Final.pdf: 4406644 bytes, checksum: 0baf7d2721f4cabcec877505e31b18d1 (MD5) Previous issue date: 2018-02-28<br>O objetivo deste estudo foi avaliar o emprego das técnicas Random Forest (RF) e Sistema Neuro-Fuzzy (SNF) na prognose da produção florestal. Os dados utilizados foram provenientes de inventários florestais contínuos conduzidos em povoamentos de clones de eucalipto, localizados no sul da Bahia. O processamento dos dados foi realizado no software Matlab R2016a. Os dados for

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Kindbom, Hannes. "LSTM vs Random Forest for Binary Classification of Insurance Related Text." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252748.

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The field of natural language processing has received increased attention lately, but less focus is put on comparing models, which differ in complexity. This thesis compares Random Forest to LSTM, for the task of classifying a message as question or non-question. The comparison was done by training and optimizing the models on historic chat data from the Swedish insurance company Hedvig. Different types of word embedding were also tested, such as Word2vec and Bag of Words. The results demonstrated that LSTM achieved slightly higher scores than Random Forest, in terms of F1 and accuracy. The mo

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Verica, Weverton Rodrigo. "Mapeamento semiautomático por meio de padrão espectro-temporal de áreas agrícolas e alvos permanentes com evi/modis no Paraná." Universidade Estadual do Oeste do Paraná, 2018. http://tede.unioeste.br/handle/tede/3916.

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Submitted by Neusa Fagundes (neusa.fagundes@unioeste.br) on 2018-09-06T19:38:50Z No. of bitstreams: 2 Weverton_Verica2018.pdf: 4544186 bytes, checksum: 766200b4dea97433d3d88b08cbe3e548 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)<br>Made available in DSpace on 2018-09-06T19:38:50Z (GMT). No. of bitstreams: 2 Weverton_Verica2018.pdf: 4544186 bytes, checksum: 766200b4dea97433d3d88b08cbe3e548 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-02-16<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -

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Sjöqvist, Hugo. "Classifying Forest Cover type with cartographic variables via the Support Vector Machine, Naive Bayes and Random Forest classifiers." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-58384.

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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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Strobl, Carolin, Anne-Laure Boulesteix, Achim Zeileis, and Torsten Hothorn. "Bias in random forest variable importance measures: Illustrations, sources and a solution." BioMed Central Ltd, 2007. http://dx.doi.org/10.1186/1471-2105-8-25.

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Background: 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 of measurement or their number of categories. This is particularly importan

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Alkazaz, Ayham, and Kharouki Marwa Saado. "Evaluation of Adaptive random forest algorithm for classification of evolving data stream." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-283114.

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In the era of big data, online machine learning algorithms have gained more and more traction from both academia and industry. In multiple scenarios decisions and predictions has to be made in near real-time as data is observed from continuously evolving data streams. Offline learning algorithms fall short in different ways when it comes to handling such problems. Apart from the costs and difficulties of storing these data streams in storage clusters and the computational difficulties associated with retraining the models each time new data is observed in order to keep the model up to date, th

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Tramontin, Davide <1992&gt. "Random forest implementation for classification analysis: default predictions applied to Italian companies." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/17720.

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The growing importance of big data and the increased environment complexity have led to an increase in the implementation machine learning algorithms, given their ability to efficiently deal with entangled situations. This study contributes to the framework regarding the application of random forests and other machine learning algorithms. Specifically, the topic of research is company failure and probability of default. The major impact that the firm’s default has on businesses, markets, and societies, underlines the importance of developing models which predict the probability of default. Thi

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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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Almer, Oscar Erik Gabriel. "Automated application-specific optimisation of interconnects in multi-core systems." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/7622.

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In embedded computer systems there are often tasks, implemented as stand-alone devices, that are both application-specific and compute intensive. A recurring problem in this area is to design these application-specific embedded systems as close to the power and efficiency envelope as possible. Work has been done on optimizing singlecore systems and memory organisation, but current methods for achieving system design goals are proving limited as the system capabilities and system size increase in the multi- and many-core era. To address this problem, this thesis investigates machine learning ap

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Rörbrink, Malin. "Improving detection of promising unrefined protein docking complexes." Thesis, Linköpings universitet, Bioinformatik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-133633.

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Understanding protein-protein interaction (PPI) is important in order to understand cellular processes. X-ray crystallography and mutagenesis, expensive methods both in time and resources, are the most reliable methods for detecting PPI. Computational approaches could, therefore, reduce resources and time spent on detecting PPIs. During this master thesis a method, cProQPred, was created for scoring how realistic coarse PPI models are. cProQPred use the machine learning method Random Forest trained on previously calculated features from the programs ProQDock and InterPred. By combining some of

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Valbi, Eleonora. "Analysis and forecasting of the structure of marine phytoplankton assemblages using innovative molecular techniques of NGS (Next Generation Sequencing) and Machine Learning." Doctoral thesis, Urbino, 2020. http://hdl.handle.net/11576/2673494.

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Elghazel, Wiem. "Wireless sensor networks for Industrial health assessment based on a random forest approach." Thesis, Besançon, 2015. http://www.theses.fr/2015BESA2055/document.

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Une maintenance prédictive efficace se base essentiellement sur la fiabilité des données de surveillance.Dans certains cas, la surveillance des systèmes industriels ne peut pas être assurée à l’aide de capteurs individuels ou filaires. Les Réseaux de Capteurs Sans Fil (RCSF) sont alors une alternative. Vu la nature de communication dans ces réseaux, la perte de données est très probable. Nous proposons un algorithme distribué pour la survie des données dans le réseau. Cet algorithme réduit le risque d’une perte totale des paquets de données et assure la continuité du fonctionnement du réseau.

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Dyer, Ross. "Predicting residential demand: applying random forest to predict housing demand in Cape Town." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29602.

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The literature shows that Random Forest is a suitable technique to predict a target variable for a household with completely unseen characteristics. The models produced in this paper show that the characteristics of a household can be used to predict the Type of Dwelling, the Tenure and the Number of Bedrooms to varying degrees of accuracy. While none of the sets of models produced indicate a high degree of predictive accuracy relative to hurdle rates, the paper does demonstrate the value that the Random Forest technique offers in moving closer to an understanding of the complex nature of hous

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Mussumeci, Elisa. "A machine learning approach to dengue forecasting: comparing LSTM, Random Forest and Lasso." reponame:Repositório Institucional do FGV, 2018. http://hdl.handle.net/10438/24093.

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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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Abd, El Meguid Mostafa. "Unconstrained facial expression recognition in still images and video sequences using Random Forest classifiers." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=107692.

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The aim of this project is to construct and implement a comprehensive facial expression detection and classification framework through the use of a proprietary face detector (PittPatt) and a novel classifier consisting of a set of Random Forests paired with either support vector machine or k-nearest neighbour labellers. The system should perform at real-time rates under unconstrained image conditions, with no intermediate human intervention. The still-image Binghamton University 3D Facial Expression database was used for training purposes, while a number of other expression-labelled video data

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Arnroth, Lukas, and Dennis Jonni Fiddler. "Supervised Learning Techniques : A comparison of the Random Forest and the Support Vector Machine." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-274768.

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This thesis examines the performance of the support vector machine and the random forest models in the context of binary classification. The two techniques are compared and the outstanding one is used to construct a final parsimonious model. The data set consists of 33 observations and 89 biomarkers as features with no known dependent variable. The dependent variable is generated through k-means clustering, with a predefined final solution of two clusters. The training of the algorithms is performed using five-fold cross-validation repeated twenty times. The outcome of the training process rev

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Oliveira, Matheus Felipe. "Mapeamento digital de solos da quadrícula de Ribeirão Preto - SP pelo método Random Forest /." Jaboticabal, 2015. http://hdl.handle.net/11449/154733.

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Orientador: José Eduardo Corá<br>Banca: Célia Regina Paes Bueno<br>Banca: Waldir de Carvalho Junior<br>Banca: Antonio Sérgio Ferraudo<br>Resumo: O presente estudo buscou desenvolver um modelo capaz de compreender as relações solo-paisagem para a predição de classes de solo das folhas do IBGE de Ribeirão Preto, Serrana, Cravinhos e Bonfim Paulista, que constituem a quadrícula de Ribeirão Preto. Para isto, foram utilizadas informações contidas em um mapa pedológico convencional semidetalhado na escala 1:100.000, um Modelo Digital de Elevação (MDE) com resolução espacial de 30 metros, além do map

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Oliveira, Matheus Felipe [UNESP]. "Mapeamento digital de solos da quadrícula de Ribeirão Preto - SP pelo método Random Forest." Universidade Estadual Paulista (UNESP), 2015. http://hdl.handle.net/11449/154733.

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Made available in DSpace on 2018-07-27T18:26:18Z (GMT). No. of bitstreams: 0 Previous issue date: 2015-12-08. Added 1 bitstream(s) on 2018-07-27T18:30:47Z : No. of bitstreams: 1 000881014.pdf: 6148920 bytes, checksum: 5c7e453ecdfb25f9189e533208588ad1 (MD5)<br>O presente estudo buscou desenvolver um modelo capaz de compreender as relações solo-paisagem para a predição de classes de solo das folhas do IBGE de Ribeirão Preto, Serrana, Cravinhos e Bonfim Paulista, que constituem a quadrícula de Ribeirão Preto. Para isto, foram utilizadas informações contidas em um mapa pedológico convencional se

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Lento, Gabriel Carneiro. "Random forest em dados desbalanceados: uma aplicação na modelagem de churn em seguro saúde." reponame:Repositório Institucional do FGV, 2017. http://hdl.handle.net/10438/18256.

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Submitted by Gabriel Lento (gabriel.carneiro.lento@gmail.com) on 2017-05-01T23:16:04Z No. of bitstreams: 1 Dissertação Gabriel Carneiro Lento.pdf: 832965 bytes, checksum: f79e7cb4e5933fd8c3a7c67ed781ddb5 (MD5)<br>Approved for entry into archive by Leiliane Silva (leiliane.silva@fgv.br) on 2017-05-04T18:39:57Z (GMT) No. of bitstreams: 1 Dissertação Gabriel Carneiro Lento.pdf: 832965 bytes, checksum: f79e7cb4e5933fd8c3a7c67ed781ddb5 (MD5)<br>Made available in DSpace on 2017-05-17T12:43:35Z (GMT). No. of bitstreams: 1 Dissertação Gabriel Carneiro Lento.pdf: 832965 bytes, checksum: f79e7cb4e59

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Wålinder, Andreas. "Evaluation of logistic regression and random forest classification based on prediction accuracy and metadata analysis." Thesis, Linnéuniversitetet, Institutionen för matematik (MA), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-35126.

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Model selection is an important part of classification. In this thesis we study the two classification models logistic regression and random forest. They are compared and evaluated based on prediction accuracy and metadata analysis. The models were trained on 25 diverse datasets. We calculated the prediction accuracy of both models using RapidMiner. We also collected metadata for the datasets concerning number of observations, number of predictor variables and number of classes in the response variable. There is a correlation between performance of logistic regression and random forest wit

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Oshiro, Thais Mayumi. "Uma abordagem para a construção de uma única árvore a partir de uma Random Forest para classificação de bases de expressão gênica." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/95/95131/tde-15102013-183234/.

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Random Forest é uma técnica computacionalmente eciente que pode operar rapida-mente sobre grandes bases de dados. Ela tem sido usada em muitos projetos de pesquisa recentes e aplicações do mundo real em diversos domínios, entre eles a bioinformática uma vez que a Random Forest consegue lidar com bases que apresentam muitos atributos e poucos exemplos. Porém, ela é de difícil compreensão para especialistas humanos de diversas áreas. A pesquisa de mestrado aqui relatada tem como objetivo criar um modelo simbólico, ou seja, uma única árvore a partir da Random Forest para a classicação de bases de

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Halmann, Marju. "Email Mining Classifier : The empirical study on combining the topic modelling with Random Forest classification." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-14710.

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Filtering out and replying automatically to emails are of interest to many but is hard due to the complexity of the language and to dependencies of background information that is not present in the email itself. This paper investigates whether Latent Dirichlet Allocation (LDA) combined with Random Forest classifier can be used for the more general email classification task and how it compares to other existing email classifiers. The comparison is based on the literature study and on the empirical experimentation using two real-life datasets. Firstly, a literature study is performed to gain ins

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Lindroth, Leonard. "Parallelization of Online Random Forest." Thesis, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21098.

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Context. Random Forests (RFs) is a very popular machine learning algorithm for mining large scale data. RFs is mainly known asan algorithm that operates in offline mode. However, in recent yearsimplementations of online random forests (ORFs) have been introduced. With multicore processors and successful implementation ofparallelism may result in increased performance of an algorithm, inrelation to its sequential implementation. Objectives. In this paper we develop and investigate the performanceof a parallel implementation of ORFs and compare the experimentalresults with its sequential counter

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Pan, Pin-Zhong, and 潘品忠. "Human Action Recognition using Random Forest." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/88721662507029523371.

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Chen, Shi-zhong, and 陳時仲. "Evaluating the Effectiveness of Random Forest Model." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/46358970356692465998.

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碩士<br>國立交通大學<br>統計學研究所<br>103<br>Random Forest is a popular machine learning algorithms. It is a decision tree model consists of multiple trees. First, we generate a specified number of tree (ex: 100), then we predict the final result by taking average of all the results (for continuous response) or by majority voting of the results (for categorical response). Random forests in R software package “randomForest” is very easy to use. As long as we choose the number of the decision tree (ntry) and the number of variables to be selected for node branching (mtry), then we can analyze the data by th

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Antonella, Mensi. "Advanced random forest approaches for outlier detection." Doctoral thesis, 2022. http://hdl.handle.net/11562/1067504.

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Outlier Detection (OD) is a Pattern Recognition task which consists of finding those patterns in a set of data which are likely to have been generated by a different mechanism than the one underlying the rest of the data. The importance of OD is visible in everyday life. Indeed, fast, and accurate detection of outliers is crucial: for example, in the electrocardiogram of a patient, an abnormality in the heart rhythm can cause severe health problems. Due to the high number of fields in which OD is needed, several approaches have been designed. Among them, Random Forest-based techniques have rai

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Dehury, Jitendra Pratap. "Random Forest-Based Intrusion Detection System (IDS)." Thesis, 2018. http://ethesis.nitrkl.ac.in/9737/1/2018_MT_216CS2154_JPDehury_Random.pdf.

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Intrusion Detection system plays an important role in network security because existing security technology is un-realistic. Most of the intrusion system (IDSs) are unable to detect intrusions due to rule based system. In this thesis random forest algorithm is used for outlier detection of network patterns. There are three intrusion techniques for intrusion detection: misuse detection , anomaly detection and hybrid detection .In this thesis the AWID-cls-R data set is used for classification. Here the aim is to reduce the false positive rate and improve the performance of intrusion detection s

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Brence, John R. "Analysis of robust measures for random forest regression /." 2004. http://wwwlib.umi.com/dissertations/fullcit/3131453.

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Lin, Pa-Hsun, and 林伯勳. "Fire and Smoke Detection Using Random Forest Algorithm." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/56813956821976269774.

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碩士<br>國立暨南國際大學<br>資訊工程學系<br>101<br>Along with the progress of computer computation capabilities, sophisticated image processing/understanding methods have been developed and the functions of intelligent video surveillance systems have been greatly extended. In this thesis, we develop a video-based fire and smoke detection system based on the random forest algorithm. We use the distinct color and image variation properties of fire/smoke to select candidate regions. Then, image features of texture and motion patterns of the candidate regions are analyzed to determine any fire/smoke region. We pr

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Chien, Chia-Chih, and 簡嘉志. "License plate recognition using the random forest algorithm." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/21636450711897378754.

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碩士<br>國立暨南國際大學<br>資訊工程學系<br>101<br>In this thesis, we study the car license plate recognition (LPR) problem which consists of a license plate localization sub-problem and a license character recognition sub-problem. We develop a heuristic method to detect license plate candidates by using mathematical morphology operations to filter edge detection results. Character recognition is accomplished by using the random forest algorithm which is trained with a huge number of synthesized character images. Since the random forest algorithm is very efficient, we use an exhaustive search strategy to d

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Liu, Meng-Hsin, and 劉孟鑫. "3D fingertip detection based on random decision forest." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/78861501850356031498.

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碩士<br>中原大學<br>資訊工程研究所<br>103<br>Hand gesture is one of the most intuitive ways to interact with machine. However, traditional 2D hand gesture recognition is very sensitive to occlusions and changes in viewpoint. The 3D localization of fingertips and palm can be helpful for hand gesture recognition under different viewpoints. In this study, we propose a new fingertip detection algorithm using two-stage random decision forest (RDF). In the first stage, local depth difference pattern (LDDP) and 3D geodesic shortest path (GSP) are adopted for training a finger pixel classifier. Two spatial and tem

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Wu, Feng-Jen, and 吳豐仁. "Optimal Operation Strategy of Chillers Using Random Forest." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/pe54fw.

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碩士<br>國立臺北科技大學<br>能源與冷凍空調工程系<br>106<br>According to the Energy Bureau of the Ministry of Economic Affairs, air-conditioning energy consumption accounts for more than 40% of the energy consumption of the entire building, and the energy consumption of chiller plant accounts for about 50 to 60% of the energy consumption of air-conditioning systems. Therefore, how to reduce the need for chiller plant is unnecessary. The energy consumption has made the effective use of energy a very important and urgent research topic. For a long time, the operating personnel of the central air-conditioning system

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HUNG, CHENG-WEI, and 洪政緯. "Forecasting New Products Selling Level by Random Forest." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/4rxq85.

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碩士<br>國立交通大學<br>工業工程與管理系所<br>107<br>The most common problem in the clothing industry is that the products must be manufactured in advance and transferred to the sales shop for sales. The underwear industry does not produce all the products at one time, but after a period of trial sales, it is handed over to the company. Subjectively determine whether to continue to produce the product, and the wrong decision to turn the order will lead to high inventory of goods, causing damage to the company's overall operating interests. This study describes the purpose and motivation of the research from t

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Joshi, Ajjen Das. "A random forest approach to segmenting and classifying gestures." Thesis, 2014. https://hdl.handle.net/2144/15405.

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This thesis investigates a gesture segmentation and recognition scheme that employs a random forest classification model. A complete gesture recognition system should localize and classify each gesture from a given gesture vocabulary, within a continuous video stream. Thus, the system must determine the start and end points of each gesture in time, as well as accurately recognize the class label of each gesture. We propose a unified approach that performs the tasks of temporal segmentation and classification simultaneously. Our method trains a random forest classification model to recognize ge

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