Relevant bibliographies by topics / Extreme Gradient Boosting Classifier / Dissertations / Theses
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Dissertations / Theses on the topic 'Extreme Gradient Boosting Classifier'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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1

Nikolaou, Nikolaos. "Cost-sensitive boosting : a unified approach." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/costsensitive-boosting-a-unified-approach(ae9bb7bd-743e-40b8-b50f-eb59461d9d36).html.

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In this thesis we provide a unifying framework for two decades of work in an area of Machine Learning known as cost-sensitive Boosting algorithms. This area is concerned with the fact that most real-world prediction problems are asymmetric, in the sense that different types of errors incur different costs. Adaptive Boosting (AdaBoost) is one of the most well-studied and utilised algorithms in the field of Machine Learning, with a rich theoretical depth as well as practical uptake across numerous industries. However, its inability to handle asymmetric tasks has been the subject of much criticis
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Al-Mter, Yusur. "Automatic Prediction of Human Age based on Heart Rate Variability Analysis using Feature-Based Methods." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166139.

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Heart rate variability (HRV) is the time variation between adjacent heartbeats. This variation is regulated by the autonomic nervous system (ANS) and its two branches, the sympathetic and parasympathetic nervous system. HRV is considered as an essential clinical tool to estimate the imbalance between the two branches, hence as an indicator of age and cardiac-related events.This thesis focuses on the ECG recordings during nocturnal rest to estimate the influence of HRV in predicting the age decade of healthy individuals. Time and frequency domains, as well as non-linear methods, are explored to
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3

Zhang, Yi. "Strategies for Combining Tree-Based Ensemble Models." NSUWorks, 2017. http://nsuworks.nova.edu/gscis_etd/1021.

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Ensemble models have proved effective in a variety of classification tasks. These models combine the predictions of several base models to achieve higher out-of-sample classification accuracy than the base models. Base models are typically trained using different subsets of training examples and input features. Ensemble classifiers are particularly effective when their constituent base models are diverse in terms of their prediction accuracy in different regions of the feature space. This dissertation investigated methods for combining ensemble models, treating them as base models. The goal is
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Andeta, Jemal Ahmed. "Road-traffic accident prediction model : Predicting the Number of Casualties." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20146.

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Efficient and effective road traffic prediction and management techniques are crucial in intelligent transportation systems. It can positively influence road advancement, safety enhancement, regulation formulation, and route planning to save living things in advance from road traffic accidents. This thesis considers road safety by predicting the number of casualties if an accident occurs using multiple traffic accident attributes. It helps individuals (drivers) or traffic offices to adjust and control their contributions for the occurrence of an accident before emerging it. Three candidate alg
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Sseguya, Raymond. "Forecasting anomalies in time series data from online production environments." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166044.

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Anomaly detection on time series forecasts can be used by many industries in especially forewarning systems that can predict anomalies before they happen. Infor (Sweden) AB is software company that provides Enterprise Resource Planning cloud solutions. Infor is interested in predicting anomalies in their data and that is the motivation for this thesis work. The general idea is firstly to forecast the time series and then secondly detect and classify anomalies on the forecast. The first part is time series forecasting and the second part is anomaly detection and classification done on the forec
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Oldenkamp, Emiel. "Using supervised learning methods to predict the stop duration of heavy vehicles." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-50977.

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In this thesis project, we attempt to predict the stop duration of heavy vehicles using data based on GPS positions collected in a previous project. All of the training and prediction is done in AWS SageMaker, and we explore possibilities with Linear Learner, K-Nearest Neighbors and XGBoost, all of which are explained in this paper. Although we were not able to construct a production-grade model within the time frame of the thesis, we were able to show that the potential for such a model does exist given more time, and propose some suggestions for the paths one can take to improve on the endpo
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7

Peng, I.-Hsuan, and 彭毅軒. "Gradient Boosting Classifier based on Gaussian Process." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/uyjc7c.

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碩士<br>國立臺灣大學<br>資訊管理學研究所<br>107<br>Gaussian process (GP) is mainly used to solve regression and classification problems in machine learning. GP is a nonparametric model with good prediction performance and wide applications. However, GP has an obvious drawback: high time complexity. This drawback makes it inappropriate for large data. In this work, we combine Gaussian process and gradient boosting to form Gradient Boosting Gaussian Process Classifier (GBGPC), then apply it to classification problems. The experiment results show that the proposed algorithm can largely improve training efficienc
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ZHUANG, BO-SHENG, and 莊博勝. "Demand Forecasting of Notebook Component Spare parts by Using Extreme Gradient Boosting." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/kd26za.

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碩士<br>國立臺北科技大學<br>工業工程與管理系<br>107<br>In recent years, due to the slowdown in the growth of notebook computers and tablet PCs, the performance of major brands has fallen into a bottleneck in research and developments. Since notebook computers are still high-priced products, and products become more sophisticated than desktop computers. In addition, the differences in products make assembly and maintenance to different degrees of difficulty, leading to an extremely high competition in the notebook market. In the past decade, notebook computer repair components often suffered from out of stock or
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Wu, Guan-Jhih, and 吳冠鋕. "Constructing a Credit Risk Assessment Model for Financial Institution by eXtreme Gradient Boosting Decision Tree." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/4sgzcr.

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10

Ou, Ming-Hong, and 歐明鴻. "Constructing a TFT-LCD Panel Classification Model for Automatic Optical Inspection using eXtreme Gradient Boosting Decision Tree." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/52npc3.

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碩士<br>國立交通大學<br>工業工程與管理系所<br>105<br>Thin film transistor liquid crystal display (TFT-LCD) panel is a key component in many electronic products. Its quality determines the value of follow-up products. Automatic optical inspection (AOI) plays an important role in Industry 4.0 and it has been introduced to screen out bad TFT-LCD panels during quality inspection. However most of the exist literature related to applying AOI in TFT-LCD panel inspection focus on defects like scratches, Mura, particles, etc. Few of them studied defective pixels on TFT-LCD panel. Therefore, this study aims to apply
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11

Bureš, Michal. "Strojové učení v algoritmickém obchodování." Master's thesis, 2021. http://www.nusl.cz/ntk/nusl-438032.

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This thesis is dedicated to the application of machine learning methods to algorithmic trading. We take inspiration from intraday traders and implement a system that predicts future price based on candlestick patterns and technical indicators. Using forex and US stocks tick data we create multiple aggregated bar representations. From these bars we construct original features based on candlestick pattern clustering by K-Means and long-term features derived from standard technical indicators. We then setup regression and classification tasks for Extreme Gradient Boosting models. From their predi
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12

(9778355), Raja Avula. "Towards Accurate Modelling of Customer Purchase Behaviour in E-commerce: An Enhanced Machine Learning Approach." Thesis, 2025. https://figshare.com/articles/thesis/Towards_Accurate_Modelling_of_Customer_Purchase_Behaviour_in_E-commerce_An_Enhanced_Machine_Learning_Approach/29333417.

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<p dir="ltr">The landscape of e-commerce has revolutionized retail, simplifying the customer purchasing journey while generating vast amounts of behavioral data. Despite this, accurately predicting customer purchasing activities remains a significant challenge. This thesis presents the findings of a completed research project aimed at developing a machine learning-based model to understand and predict customer shopping behavior in e-commerce environments. The study focuses on three primary objectives: firstly, constructing a comprehensive dataset tailored to the research model; secondly, train
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Nascimento, Matheus Lopes do. "Investigation of Geothermal Potential Zones with Machine Learning in Mainland Portugal." Master's thesis, 2022. http://hdl.handle.net/10362/134617.

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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies<br>Earth’s internal heat is explored to produce electricity or used directly in industrial processes or residencies. It is considered to be renewable and cleaner than fossil fuels and has great importance to pursue environmental goals. The exploration phase of geothermal resources is complex and expensive. It requires field surveys, geological, geophysical and geochemical analysis, as well as drilling campaigns. Geospatial data and technologies have been used to targe
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Madrid, Ernesto Javier Aguilar. "Short-Term Electricity Demand Forecasting with Machine Learning." Master's thesis, 2021. http://hdl.handle.net/10362/120626.

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Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics<br>An accurate short-term load forecasting (STLF) is one of the most critical inputs for power plant units’ planning commitment. STLF reduces the overall planning uncertainty added by the intermittent production of renewable sources; thus, it helps to minimize the hydro-thermal electricity production costs in a power grid. Although there is some research in the field and even several research applications, there is a continual need
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Roy, Bhupendra. "Identifying Deception in Online Reviews: Application of Machine Learning, Deep Learning and Natural Language Processing." Master's thesis, 2020. http://hdl.handle.net/10362/101187.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics<br>Customers increasingly rate, review and research products online, (Jansen 2010). Consequently, websites containing consumer reviews are becoming targets of opinion spam. Now-a-days, people are paid money to write fake positive review online, to misguide customer and to augment sales revenue. Alternatively, people are also paid to pose as customers and to post negative fake reviews with the objective to slash competitors. These have caused menace in social media and o
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16

Molisse, Giulia. "Above ground biomass and carbon sequestration estimation -Implementation of a sentinel-2 based exploratory workflow." Master's thesis, 2021. http://hdl.handle.net/10362/113902.

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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies<br>This work presents a Sentinel-2 based exploratory work ow for the estimation of Above Ground Biomass (AGB) and Carbon Sequestration (CS) in a subtropical forest. In the last decades, remote sensing-based studies on AGB have been widely investigated alongside with a variety of sensors, features and Machine Learning (ML) algorithms. Up-to-date and reliable mapping of such measures have been increasingly required by international commitments under the climate co
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