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Rozprawy doktorskie na temat „ENSEMBLE LEARNING MODELS”

Autor: Grafiati

Data publikacji: 11 września 2023

Data aktualizacji: 26 lipca 2025

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1

He, Wenbin. "Exploration and Analysis of Ensemble Datasets with Statistical and Deep Learning Models." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574695259847734.

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Kim, Jinhan. "J-model : an open and social ensemble learning architecture for classification." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/7672.

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Ensemble learning is a promising direction of research in machine learning, in which an ensemble classifier gives better predictive and more robust performance for classification problems by combining other learners. Meanwhile agent-based systems provide frameworks to share knowledge from multiple agents in an open context. This thesis combines multi-agent knowledge sharing with ensemble methods to produce a new style of learning system for open environments. We now are surrounded by many smart objects such as wireless sensors, ambient communication devices, mobile medical devices and even inf

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Gharroudi, Ouadie. "Ensemble multi-label learning in supervised and semi-supervised settings." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1333/document.

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L'apprentissage multi-label est un problème d'apprentissage supervisé où chaque instance peut être associée à plusieurs labels cibles simultanément. Il est omniprésent dans l'apprentissage automatique et apparaÃ®t naturellement dans de nombreuses applications du monde réel telles que la classification de documents, l'étiquetage automatique de musique et l'annotation d'images. Nous discutons d'abord pourquoi les algorithmes multi-label de l'etat-de-l'art utilisant un comité de modèle souffrent de certains inconvénients pratiques. Nous proposons ensuite une nouvelle stratégie pour construire et

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Henriksson, Aron. "Ensembles of Semantic Spaces : On Combining Models of Distributional Semantics with Applications in Healthcare." Doctoral thesis, Stockholms universitet, Institutionen för data- och systemvetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-122465.

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Distributional semantics allows models of linguistic meaning to be derived from observations of language use in large amounts of text. By modeling the meaning of words in semantic (vector) space on the basis of co-occurrence information, distributional semantics permits a quantitative interpretation of (relative) word meaning in an unsupervised setting, i.e., human annotations are not required. The ability to obtain inexpensive word representations in this manner helps to alleviate the bottleneck of fully supervised approaches to natural language processing, especially since models of distribu

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Chakraborty, Debaditya. "Detection of Faults in HVAC Systems using Tree-based Ensemble Models and Dynamic Thresholds." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1543582336141076.

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Li, Qiongzhu. "Study of Single and Ensemble Machine Learning Models on Credit Data to Detect Underlying Non-performing Loans." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297080.

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In this paper, we try to compare the performance of two feature dimension reduction methods, the LASSO and PCA. Both simulation study and empirical study show that the LASSO is superior to PCA when selecting significant variables. We apply Logistics Regression (LR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT) and their corresponding ensemble machines constructed by bagging and adaptive boosting (adaboost) in our study. Three experiments are conducted to explore the impact of class-unbalanced data set on all models. Empirical study indicates that when the p

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Franch, Gabriele. "Deep Learning for Spatiotemporal Nowcasting." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/295096.

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Nowcasting – short-term forecasting using current observations – is a key challenge that human activities have to face on a daily basis. We heavily rely on short-term meteorological predictions in domains such as aviation, agriculture, mobility, and energy production. One of the most important and challenging task for meteorology is the nowcasting of extreme events, whose anticipation is highly needed to mitigate risk in terms of social or economic costs and human safety. The goal of this thesis is to contribute with new machine learning methods to improve the spatio-temporal precision of now

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Franch, Gabriele. "Deep Learning for Spatiotemporal Nowcasting." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/295096.

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Nowcasting – short-term forecasting using current observations – is a key challenge that human activities have to face on a daily basis. We heavily rely on short-term meteorological predictions in domains such as aviation, agriculture, mobility, and energy production. One of the most important and challenging task for meteorology is the nowcasting of extreme events, whose anticipation is highly needed to mitigate risk in terms of social or economic costs and human safety. The goal of this thesis is to contribute with new machine learning methods to improve the spatio-temporal precision of now

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Ekström, Linus, and Andreas Augustsson. "A comperative study of text classification models on invoices : The feasibility of different machine learning algorithms and their accuracy." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15647.

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Text classification for companies is becoming more important in a world where an increasing amount of digital data are made available. The aim is to research whether five different machine learning algorithms can be used to automate the process of classification of invoice data and see which one gets the highest accuracy. Algorithms are in a later stage combined for an attempt to achieve higher results. N-grams are used, and results are compared in form of total accuracy of classification for each algorithm. A library in Python, called scikit-learn, implementing the chosen algorithms, was used

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Lundberg, Jacob. "Resource Efficient Representation of Machine Learning Models : investigating optimization options for decision trees in embedded systems." Thesis, Linköpings universitet, Statistik och maskininlärning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-162013.

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Combining embedded systems and machine learning models is an exciting prospect. However, to fully target any embedded system, with the most stringent resource requirements, the models have to be designed with care not to overwhelm it. Decision tree ensembles are targeted in this thesis. A benchmark model is created with LightGBM, a popular framework for gradient boosted decision trees. This model is first transformed and regularized with RuleFit, a LASSO regression framework. Then it is further optimized with quantization and weight sharing, techniques used when compressing neural networks. Th

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Olofsson, Nina. "A Machine Learning Ensemble Approach to Churn Prediction : Developing and Comparing Local Explanation Models on Top of a Black-Box Classifier." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210565.

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Churn prediction methods are widely used in Customer Relationship Management and have proven to be valuable for retaining customers. To obtain a high predictive performance, recent studies rely on increasingly complex machine learning methods, such as ensemble or hybrid models. However, the more complex a model is, the more difficult it becomes to understand how decisions are actually made. Previous studies on machine learning interpretability have used a global perspective for understanding black-box models. This study explores the use of local explanation models for explaining the individual

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Henriksson, Erik, and Kristopher Werlinder. "Housing Price Prediction over Countrywide Data : A comparison of XGBoost and Random Forest regressor models." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302535.

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The aim of this research project is to investigate how an XGBoost regressor compares to a Random Forest regressor in terms of predictive performance of housing prices with the help of two data sets. The comparison considers training time, inference time and the three evaluation metrics R2, RMSE and MAPE. The data sets are described in detail together with background about the regressor models that are used. The method makes substantial data cleaning of the two data sets, it involves hyperparameter tuning to find optimal parameters and 5foldcrossvalidation in order to achieve good performance e

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Ngo, Khai Thoi. "Stacking Ensemble for auto_ml." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83547.

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Machine learning has been a subject undergoing intense study across many different industries and academic research areas. Companies and researchers have taken full advantages of various machine learning approaches to solve their problems; however, vast understanding and study of the field is required for developers to fully harvest the potential of different machine learning models and to achieve efficient results. Therefore, this thesis begins by comparing auto ml with other hyper-parameter optimization techniques. auto ml is a fully autonomous framework that lessens the knowledge prerequisi

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Ferreira, Ednaldo José. "Método baseado em rotação e projeção otimizadas para a construção de ensembles de modelos." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-27062012-161603/.

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O desenvolvimento de novas técnicas capazes de produzir modelos de predição com erros de generalização relativamente baixos é uma constante em aprendizado de máquina e áreas correlatas. Nesse sentido, a composição de um conjunto de modelos no denominado ensemble merece destaque por seu potencial teórico e empírico de minimizar o erro de generalização. Diversos métodos para construção de ensembles de modelos são encontrados na literatura. Dentre esses, o método baseado em rotação (RB) tem apresentado desempenho superior a outros clássicos. O método RB utiliza a técnica de extração de caracterís

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Top, Mame Kouna. "Analyse des modèles résines pour la correction des effets de proximité en lithographie optique." Thesis, Grenoble, 2011. http://www.theses.fr/2011GRENT007/document.

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Les progrès réalisés dans la microélectronique répondent à la problématique de la réduction des coûts de production et celle de la recherche de nouveaux marchés. Ces progrès sont possibles notamment grâce à ceux effectués en lithographie optique par projection, le procédé lithographique principalement utilisé par les industriels. La miniaturisation des circuits intégrés n’a donc été possible qu’en poussant les limites d’impression lithographique. Cependant en réduisant les largeurs des transistors et l’espace entre eux, on augmente la sensibilité du transfert à ce que l’on appelle les effets d

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Whiting, Jeffrey S. "Cognitive and Behavioral Model Ensembles for Autonomous Virtual Characters." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1873.pdf.

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Iyer, Vasanth. "Ensemble Stream Model for Data-Cleaning in Sensor Networks." FIU Digital Commons, 2013. http://digitalcommons.fiu.edu/etd/973.

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Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the

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Ali, Rozniza. "Ensemble classification and signal image processing for genus Gyrodactylus (Monogenea)." Thesis, University of Stirling, 2014. http://hdl.handle.net/1893/21734.

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This thesis presents an investigation into Gyrodactylus species recognition, making use of machine learning classification and feature selection techniques, and explores image feature extraction to demonstrate proof of concept for an envisaged rapid, consistent and secure initial identification of pathogens by field workers and non-expert users. The design of the proposed cognitively inspired framework is able to provide confident discrimination recognition from its non-pathogenic congeners, which is sought in order to assist diagnostics during periods of a suspected outbreak. Accurate identif

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Darwiche, Aiman A. "Machine Learning Methods for Septic Shock Prediction." Diss., NSUWorks, 2018. https://nsuworks.nova.edu/gscis_etd/1051.

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Sepsis is an organ dysfunction life-threatening disease that is caused by a dysregulated body response to infection. Sepsis is difficult to detect at an early stage, and when not detected early, is difficult to treat and results in high mortality rates. Developing improved methods for identifying patients in high risk of suffering septic shock has been the focus of much research in recent years. Building on this body of literature, this dissertation develops an improved method for septic shock prediction. Using the data from the MMIC-III database, an ensemble classifier is trained to identify

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Li, Jianeng. "Research on a Heart Disease Prediction Model Based on the Stacking Principle." Thesis, Högskolan Dalarna, Informatik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:du-34591.

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In this study, the prediction model based on the Stacking principle is called the Stacking fusion model. Little evidence demonstrates that the Stacking fusion model possesses better prediction performance in the field of heart disease diagnosis than other classification models. Since this model belongs to the family of ensemble learning models, which has a bad interpretability, it should be used with caution in medical diagnoses. The purpose of this study is to verify whether the Stacking fusion model has better prediction performance than stand-alone machine learning models and other ensemble

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Boulegane, Dihia. "Machine learning algorithms for dynamic Internet of Things." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT048.

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La croissance rapide de l’Internet des Objets (IdO) ainsi que la prolifération des capteurs ont donné lieu à diverses sources de données qui génèrent continuellement de grandes quantités de données et à une grande vitesse sous la forme de flux. Ces flux sont essentiels dans le processus de prise de décision dans différents secteurs d’activité et ce grâce aux techniques d’intelligence artificielle et d’apprentissage automatique afin d’extraire des connaissances précieuses et les transformer en actions pertinentes. Par ailleurs, les données sont souvent associées à un indicateur temporel, appelé

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Pouilly-Cathelain, Maxime. "Synthèse de correcteurs s’adaptant à des critères multiples de haut niveau par la commande prédictive et les réseaux de neurones." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG019.

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Cette thèse porte sur la commande des systèmes non linéaires soumis à des contraintes non différentiables ou non convexes. L'objectif est de pouvoir réaliser une commande permettant de considérer tout type de contraintes évaluables en temps réel.Pour répondre à cet objectif, la commande prédictive a été utilisée en ajoutant des fonctions barrières à la fonction de coût. Un algorithme d'optimisation sans gradient a permis de résoudre ce problème d'optimisation. De plus, une formulation permettant de garantir la stabilité et la robustesse vis-à-vis de perturbations a été proposée dans le cadre d

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Duncan, Andrew Paul. "The analysis and application of artificial neural networks for early warning systems in hydrology and the environment." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/17569.

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Artificial Neural Networks (ANNs) have been comprehensively researched, both from a computer scientific perspective and with regard to their use for predictive modelling in a wide variety of applications including hydrology and the environment. Yet their adoption for live, real-time systems remains on the whole sporadic and experimental. A plausible hypothesis is that this may be at least in part due to their treatment heretofore as “black boxes” that implicitly contain something that is unknown, or even unknowable. It is understandable that many of those responsible for delivering Early Warni

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Bellani, Carolina. "Predictive churn models in vehicle insurance." Master's thesis, 2019. http://hdl.handle.net/10362/90767.

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Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics<br>The goal of this project is to develop a predictive model to reduce customer churn from a company. In order to reduce churn, the model will identify customers who may be thinking of ending their patronage. The model also seeks to identify the reasons behind the customers decision to leave, to enable the company to take appropriate counter measures. The company in question is an insurance company in Portugal, Tranquilidade, and this project will focus in particula

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JAWA, MISHA. "COMPARISION OF ENSEMBLE LEARNING MODELS AND IMPACT OF DATA BALANCING TECHNIQUE FOR SOFTWARE EFFORT ESTIMATION." Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19229.

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Project management is a critical component of every software project's success. Estimating the cost and effort of software development at the outset of the project is one of the most important responsibilities in software project management. Estimating effort allows project managers to more effectively manage resources and activities. The primary purpose of this study was to construct and compare the usage of two common ensemble approaches (bagging and boosting) to improve estimator accuracy and to study the impact of Synthetic Minority Over-Sampling Technique for Regression (SMO

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Amaro, Miguel Mendes. "Credit scoring: comparison of non‐parametric techniques against logistic regression." Master's thesis, 2020. http://hdl.handle.net/10362/99692.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence<br>Over the past decades, financial institutions have been giving increased importance to credit risk management as a critical tool to control their profitability. More than ever, it became crucial for these institutions to be able to well discriminate between good and bad clients for only accepting the credit applications that are not likely to default. To calculate the probability of default of a particular client, m

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Santos, Esdras Christo Moura dos. "Predictive modelling applied to propensity to buy personal accidents insurance products." Master's thesis, 2018. http://hdl.handle.net/10362/37698.

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Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics<br>Predictive models have been largely used in organizational scenarios with the increasing popularity of machine learning. They play a fundamental role in the support of customer acquisition in marketing campaigns. This report describes the development of a propensity to buy model for personal accident insurance products. The entire process from business understanding to the deployment of the final model is analyzed with the objective of linking the theory to practice

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Gau, Olivier. "Ensemble learning with GSGP." Master's thesis, 2020. http://hdl.handle.net/10362/93780.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics<br>The purpose of this thesis is to conduct comparative research between Genetic Programming (GP) and Geometric Semantic Genetic Programming (GSGP), with different initialization (RHH and EDDA) and selection (Tournament and Epsilon-Lexicase) strategies, in the context of a model-ensemble in order to solve regression optimization problems. A model-ensemble is a combination of base learners used in different ways to solve a problem. The most common ensemble is the mean,

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Nožička, Michal. "Ensemble learning metody pro vývoj skóringových modelů." Master's thesis, 2018. http://www.nusl.cz/ntk/nusl-382813.

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Credit scoring is very important process in banking industry during which each potential or current client is assigned credit score that in certain way expresses client's probability of default, i.e. failing to meet his or her obligations on time or in full amount. This is a cornerstone of credit risk management in banking industry. Traditionally, statistical models (such as logistic regression model) are used for credit scoring in practice. Despite many advantages of such approach, recent research shows many alternatives that are in some ways superior to those traditional models. This master

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Milioli, Heloisa Helena. "Breast cancer intrinsic subtypes: a critical conception in bioinformatics." Thesis, 2017. http://hdl.handle.net/1959.13/1350957.

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Research Doctorate - Doctor of Philosophy (PhD)<br>Breast cancers have been uncovered by high-throughput technologies that allow the investigation at the genomic, transcriptomic and proteomic levels. In the early 2000s, the gene expression profiling has led to the classification of five intrinsic subtypes: luminal A, luminal B, HER2-enriched, normal like and basal-like. A decade later, the spectrum of copy number aberrations has further expanded the heterogeneous architecture of this disease with the identification of 10 integrative clusters (IntClusts). The referred classifications aim at exp

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Abreu, Mariana da Conceição Ferreira. "Modelos de Avaliação de Risco de Crédito: Aplicação de Machine Learning." Master's thesis, 2020. http://hdl.handle.net/10316/94723.

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Trabalho de Projeto do Mestrado em Economia apresentado à Faculdade de Economia<br>Existem vários métodos que ao longo dos anos tem sido empregues na avaliação de risco de crédito, sobretudo, metodologias tradicionais como o Modelo de Análise Discriminante (ADi), Modelo Logit e Modelo Probit, e metodologias mais sofisticadas de Machine Learning, como Árvores de Classificação (AC), Random Forests (RF), Redes Neuronais (RN) e Support Vector Machines (SVM). Na revisão de literatura são apresentados alguns estudos que recorrem a metodologias tradicionais e a metodologias de Machine Learning. Estas

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Huang, Hong-Zhou, and 黃弘州. "Nonintrusive Appliance Recognition Algorithm based on Ensemble Learning Model." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/3ftb3d.

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碩士<br>國立中興大學<br>資訊科學與工程學系<br>103<br>In this paper, a non-intrusive appliance load monitoring (NILM) scheme based on the Adaboot ensemble algorithm for cheaper and low frequency meter is developed. In order to apply the NILM scheme we need to extract features for appliances. However, it is a challenging task if we want to know the states for each appliance at home just from information of single point aggregate power meter. In literature, it is usually done by applying high frequency meter to extract high frequency feature, e.g., harmonics and electromagnetic interference, to make recognition

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Chi, Tsai-Lin, and 季彩琳. "Using Ensemble Learning Model for Classifying Fiduciary Purchasing Behavior." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/49166414836032969056.

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碩士<br>輔仁大學<br>企業管理學系管理學碩士班<br>102<br>In financial industry, the environment is getting much harsher than ever before. To be outstanding in financial sector, bankers have been tried to satisfy the need of customers as they can whether in service quality or in service area. Considering the characteristics of the consumer credit loans which are less risky and thriving, bankers are keen to sell consumer credit loans. The objective of the proposed study is to explore the performance of classification model for classifying fiduciary purchasing behavior using ensemble learning techniques. This study

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SYU, HUAN-YU, and 許桓瑜. "Prediction Model of Narcolepsy Based on Ensemble Learning Approach." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/2d9ud3.

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碩士<br>國立臺北護理健康大學<br>資訊管理研究所<br>106<br>The advent of the era of precision medicine shows that the diagnosis of diseases tends to be personalized and customized. Nowadays, the combination of medicine and information is the trend of the times, and Narcolepsy is a kind of Hypersomnia. Patients often have symptoms such as excessive daytime sleepiness, cataplexy, hypnagogic hallucination, Narcolepsy must be diagnosed by multiple tests of sleep, multi-stage sleep test, etc. Most of the studies related to narcolepsy use only partial or specific tests. In this study, about ten kinds of measurement and

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CHIU, YI-HAN, and 邱奕涵. "Using Ensemble Learning to Build the Sales Forecast Model of Baking Industry." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/57v4jt.

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碩士<br>國立高雄第一科技大學<br>行銷與流通管理系碩士班<br>106<br>Recently, the value of bakery industry output is being on the rise in Taiwan. Nowadays, there is a growing focus on healthy diet. Given this, the research use sales data which is from healthy bakeries to build the sales forecast model of baking industry. Besides, the research tries to use data visualization and feature selection to examine each bakery. Eventually, through Ensemble Learning to build the better forecast model of baking industry. The result demonstrate that technique XGBoost is better than other model. In addition, the result wou

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Huang, Yong-Jhih, and 黃雍智. "Applying Deep Learning and Ensemble Learning to Construct Spectrum and Cepstrum of Filtered Phonocardiogram Prediction Model." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/535usy.

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碩士<br>國立中興大學<br>資訊管理學系所<br>106<br>Coronary artery disease is a common chronic disease, as known as ischemic heart disease, which is cardiac dysfunction caused by insufficient blood supply to the heart and kills countless people every year in the world. In recent years, coronary artery disease ranks first in the world’s top ten cause of death. Until now, cardiac auscultation is still an important examination for diagnosing heart diseases. Many heart diseases can be diagnosed effectively by auscultation. However, cardiac auscultation relies on the subjective experience of physicians. In order to

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Zheng, Yu-Xuan, and 鄭宇軒. "Sleep Apnea Detection Algorithm using EEG and Oximetry based on Ensemble Learning Model." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/66291000965483738888.

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碩士<br>國立中興大學<br>資訊科學與工程學系<br>105<br>The gold standard for diagnosis of sleep apnea is a formal sleep study established by the polysomnography(PSG). However the high cost and the complex steps of PSG makes a diagnosis of sleep apnea become evenmore difficult. Not to mention the shortage of devices and medical human resources. In this thesis, we propose a sleep apnea detection algorithm based on ensemble machine learning model. By using only Electroencephalography(EEG) and Oximetry, we can significantly reduce the difficulty of diagnosis and the effort of medical persons. The experimental res

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Cheng, Lu-Wen, and 程路文. "A prediction model of air pollution and Respiratory Diseases based on Ensemble learning." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/23mv9a.

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碩士<br>元智大學<br>資訊工程學系<br>106<br>The study aimed to determine whether there is an association between air pollutants levels and outpatient clinic visits with chronic obstructive pulmonary disease (COPD) in Taiwan. Data of air pollutant concentrations (PM2.5、PM10、SO2、NO2、CO、O3) were collected from air monitoring stations. We use a case-crossover study design and conditional logistic regression models with odds ratios (OR) and 95% confidence intervals(CI) for evaluating the associations between the air pollutant factor and COPD-associated OC visits. Analyses show the PM2.5, PM10, CO, NO2, SO2 had

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Chang, Hsueh-Wei, and 張學瑋. "Nonintrusive Appliance Recognition Algorithm based on Ensemble Learning Model integrating with Dynamic Time Warping." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/35192290829923038200.

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碩士<br>國立中興大學<br>資訊科學與工程學系<br>104<br>According to the research, if we can provide immediate and fine-grained power information to users, a significant reduction in the energy wastage can be achieved. Non-Intrusive Appliance Load Monitoring is an approach to reach the goal, which is more practical and feasible for typical families. In previous studies, we can discover that there were some disadvantages. First, it usually used high frequency sensor to acquire information, which made the cost of hardware higher. Second, most studies focused on the high consumption or on/off type appliances. As a r

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Silvestre, Martinho de Matos. "Three-stage ensemble model : reinforce predictive capacity without compromising interpretability." Master's thesis, 2019. http://hdl.handle.net/10362/71588.

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Thesis proposal presented as partial requirement for obtaining the Master’s degree in Statistics and Information Management, with specialization in Risk Analysis and Management<br>Over the last decade, several banks have developed models to quantify credit risk. In addition to the monitoring of the credit portfolio, these models also help deciding the acceptance of new contracts, assess customers profitability and define pricing strategy. The objective of this paper is to improve the approach in credit risk modeling, namely in scoring models to predict default events. To this end, we propose t

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Chen, Chien-Jen, and 陳建仁. "Combining Hidden Markov Model with Ensemble Learning to Predict Hidden States and Conduct Stochastic Simulation." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/mhh87z.

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碩士<br>國立交通大學<br>工業工程與管理系所<br>106<br>Taiwan’s semiconductor industry, optoelectronics industry, computers and peripheral equipment industry play an important role in the world. Additionally, the rapid development of Artificial Intelligence (AI) and Internet of Things (IoT) have also driven the growth of these industries. Although the overall industry is growing up, there is a significant gap between the firms within the industry. Therefore, this study focuses on those companies which revenues go up and down. First, Hidden Markov Model (HMM) is used to explore the company’s hidden states. Withou

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Hong, Zih-Siang, and 洪梓翔. "Using Ensemble Learning and Deep Recurrent Neural Network to Construct an Internet Forum Conversation Prediction Model." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/s67dep.

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碩士<br>中原大學<br>資訊管理研究所<br>106<br>The study on natural language dialogue or conversation involves language understanding, reasoning, and basic common sense, therefore it is one of the most challenging artificial intelligence issues. To design a common and general conversation model is even more complicated and difficult. In the past, the studies on natural language processing and dialogue mainly focused on the rule-based and machine learning-based methods. Although these methods can solve part of the dialogue problems in the specific fields, but they have their own learning bottlenecks. Until re

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Wu, Hsuan, and 吳亘. "Constructing a Risk Assessment Model for Small and Medium Enterprises by Ensemble Learning with Macroeconomic Indices." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/4573r2.

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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>Due to the high connection of the global financial system, the international financial crisis may have a significant influence on the domestic economy and increase the number of non-performing loans from financial institutions. As a result, many financial institutions have begun to construct an objective and fair risk assessment model. However, most financial institutions only take internal information about borrowing SMEs into account when constructing the model. Therefore, considering the macroeconomic environment may affect the risk of default, this thesi

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Siedel, Georg. "Evaluation von Machine-Learning-Modellen und Konzeptionierung eines Modell-Ensembles für die Vorhersage von Unfalldaten." 2020. https://tud.qucosa.de/id/qucosa%3A73972.

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In dieser Arbeit wird mittels verschiedener Methoden die Datenfusion von Unfallszenarien untersucht. Ausgangspunkt sind zwei Datensätze aus der Datenbank der polizeilichen Unfallstatistik. Im Empfängerdatensatz wird das spezifische Attribut „Unfalltyp“ entfernt, welches mithilfe des Spenderdatensatzes ergänzt werden soll. Ziel ist das Erstellen einer einheitlichen Datenbasis, deren Qualität mittels geeigneter ausgewählter Metriken bewertet wird. Als Methode der Datenfusion wird zum einen das Distance-Hot-Deck-Verfahren verwendet. Zum anderen werden vier aussichtsreiche Machine Learning Verfahr

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Frazão, Xavier Marques. "Deep learning model combination and regularization using convolutional neural networks." Master's thesis, 2014. http://hdl.handle.net/10400.6/5605.

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Convolutional neural networks (CNNs) were inspired by biology. They are hierarchical neural networks whose convolutional layers alternate with subsampling layers, reminiscent of simple and complex cells in the primary visual cortex [Fuk86a]. In the last years, CNNs have emerged as a powerful machine learning model and achieved the best results in many object recognition benchmarks [ZF13, HSK+12, LCY14, CMMS12]. In this dissertation, we introduce two new proposals for convolutional neural networks. The first, is a method to combine the output probabilities of CNNs which we call Weighted

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Ashofteh, Afshin. "Data Science for Finance: Targeted Learning from (Big) Data to Economic Stability and Financial Risk Management." Doctoral thesis, 2022. http://hdl.handle.net/10362/135620.

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A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Statistics and Econometrics<br>The modelling, measurement, and management of systemic financial stability remains a critical issue in most countries. Policymakers, regulators, and managers depend on complex models for financial stability and risk management. The models are compelled to be robust, realistic, and consistent with all relevant available data. This requires great data disclosure, which is deemed to have the highest quality standards. However, stressed

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