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Relevant bibliographies by topics / Gradient boosting

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Academic literature on the topic 'Gradient boosting'

Author: Grafiati

Published: 4 June 2021

Last updated: 21 September 2024

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Journal articles on the topic "Gradient boosting"

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Friedman, Jerome H. "Stochastic gradient boosting." Computational Statistics & Data Analysis 38, no. 4 (February 2002): 367–78. http://dx.doi.org/10.1016/s0167-9473(01)00065-2.

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Biau, G., B. Cadre, and L. Rouvière. "Accelerated gradient boosting." Machine Learning 108, no. 6 (February 4, 2019): 971–92. http://dx.doi.org/10.1007/s10994-019-05787-1.

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Dombry, Clément, and Jean-Jil Duchamps. "Infinitesimal gradient boosting." Stochastic Processes and their Applications 170 (April 2024): 104310. http://dx.doi.org/10.1016/j.spa.2024.104310.

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Dahlia, Rizka, and Cucu Ika Agustyaningrum. "Perbandingan Gradient Boosting dan Light Gradient Boosting Dalam Melakukan Klasifikasi Rumah Sewa." Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) 5, no. 6 (December 29, 2022): 1016–20. http://dx.doi.org/10.32672/jnkti.v5i6.5460.

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Abstrak— Persaingan antar perusahaan tidak akan dapat terhindarkan apalagi terkait tujuan perusahaan dalam mendapatkan omset sebesar-besarnya. Salah satu persaingan yang terjadi adalah dibidang property atau jika lebih spesifik lagi yaitu penyewaan rumah. Sebuah perusahaan harus menentukan strategi bagaimana rumah yang akan disewakan nantinya akan sebanding dengan harga pembangunan. Maka dari itu perusahaan dapat melakukan klasifikasi rumah sewa dalam menentukan hal tersebut. Penelitian ini menggunakan model Gradient Boosting dan Light Gradient Boosting. Hasil yang didapatkan adalah bahwa model Gradient Boosting adalah model yang cocok pada penelitian ini dengan mendapatkan hasil accuracy 84.38%, precision 83.33% dan recall 87.53%. Jika dilihat perbandingan dari confusion matrix, Gradient Boosting memiliki jumlah hasil prediksi data lebih besar dibanding dibanding Light Gradient Boosting.Kata kunci: Rumah Sewa, Data Mining, Gradient Boosting, Light Gradient Boosting Abstract— Competition between companies cannot be avoided, especially regarding the company's goal of getting the maximum turnover. One of the competitions that occurs is in the property sector, or more specifically, house rental. A company must determine a strategy for how the house to be rented out will be comparable to the construction price. Therefore the company can classify rental houses in determining this. This study uses the Gradient Boosting and Light Gradient Boosting models. The results obtained are that the Gradient Boosting model is a suitable model in this study with 84.38% accuracy, 83.33% precision and 87.53% recall. If you look at the comparison of the confusion matrix, Gradient Boosting has a greater number of data prediction results than Light Gradient Boosting.Keywords : House for rent, Data Mining, Gradient Boosting, Light Gradient Boosting

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Dubossarsky, E., J. H. Friedman, J. T. Ormerod, and M. P. Wand. "Wavelet-based gradient boosting." Statistics and Computing 26, no. 1-2 (May 8, 2014): 93–105. http://dx.doi.org/10.1007/s11222-014-9474-0.

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Lu, Haihao, and Rahul Mazumder. "Randomized Gradient Boosting Machine." SIAM Journal on Optimization 30, no. 4 (January 2020): 2780–808. http://dx.doi.org/10.1137/18m1223277.

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Suryana, Silvia Elsa, Budi Warsito, and Suparti Suparti. "PENERAPAN GRADIENT BOOSTING DENGAN HYPEROPT UNTUK MEMPREDIKSI KEBERHASILAN TELEMARKETING BANK." Jurnal Gaussian 10, no. 4 (December 31, 2021): 617–23. http://dx.doi.org/10.14710/j.gauss.v10i4.31335.

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Telemarketing is another form of marketing which is conducted via telephone. Bank can use telemarketing to offer its products such as term deposit. One of the most important strategy to the success of telemarketing is opting the potential customer to create effective telemarketing. Predicting the success of telemarketing can use machine learning. Gradient boosting is machine learning method with advanced decision tree. Gardient boosting involves many classification trees which are continually upgraded from previous tree. The optimal classification result cannot be separated from the role of the optimal hyperparameter. Hyperopt is Python library that can be used to tune hyperparameter effectively because it uses Bayesian optimization. Hyperopt uses hyperparameter prior distribution to find optimal hyperparameter. Data in this study including 20 independent variables and binary dependent variable which has ‘yes’ and ‘no’ classes. The study showed that gradient boosting reached classification accuracy up to 90,39%, precision 94,91%, and AUC 0,939. These values describe gradient boosting method is able to predict both classes ‘yes’ and ‘no’ relatively accurate.

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Zhu, Fei, Xiangping Wu, Yijun Lu, and Jiandong Huang. "Strength Estimation and Feature Interaction of Carbon Nanotubes-Modified Concrete Using Artificial Intelligence-Based Boosting Ensembles." Buildings 14, no. 1 (January 4, 2024): 134. http://dx.doi.org/10.3390/buildings14010134.

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The standard approach for testing ordinary concrete compressive strength (CS) is to cast samples and test them after different curing times. However, testing adds cost and time to projects, and, therefore, construction sites experience delays. Because carbon nanotubes (CNTs) vary in length, composition, diameter, and dispersion, experiment and formula fitting alone cannot reliably predict the strength of CNTs-based composites. For empirical equations or traditional statistical approaches to properly forecast complex materials’ mechanical characteristics, various significant parameters, databases, and nonlinear relationships between variables must be considered. Machine learning (ML) tools are the most advanced for accurate predictions of material behaviour. This study employed gradient boosting, light gradient boosting machine, and extreme gradient boosting techniques to forecast the CS of CNTs-modified concrete. Also, in order to explore the influence and interaction of various features, an interaction analysis was conducted. In terms of R2, gradient boosting, light gradient boosting machine, and extreme gradient boosting models proved their accuracy. Extreme gradient boosting had the highest R2 of 0.97, followed by light gradient boosting machine and gradient boosting with scores of 0.94 and 0.93, respectively. This type of research may help both academics and industry forecast material properties and influential elements, thereby reducing lab test requirements.

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ЧЕВЕРЕВА, С. А., and Е. А. КАЗАНКОВ. "COMPARATIVE ANALYSIS OF ALGORITHMS BASED ON GRADIENT BOOSTING IN THE FIELD OF SECURITY OF THE INTERNET OF THINGS." Экономика и предпринимательство, no. 7(168) (August 6, 2024): 836–39. http://dx.doi.org/10.34925/eip.2024.168.7.164.

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Целью исследования данной статьи является сравнительный анализ алгоритмов на основе градиентного бустинга, которые можно применить к решению задачи обнаружения вторжений и смягчения последствий атаки в системах Интернета вещей. В данной статье обосновывается важность повышения уровня безопасности систем Интернета вещей, подчеркиваются ограничения нынешних мер из-за нехватки ресурсов этих устройств. Предлагается использование алгоритмов машинного обучения на основе градиентного бустинга для помощи в обнаружении вторжений в сеть. Проведен эксперимент по обнаружению вторжений с помощью следующих алгоритмов: Adaptive Boosting (ADB), Gradient Descent, Extreme Gradient Boosting (XGB), Categorical Boosting (CAB) и Light Gradient Boosting (LGB). Полученные результаты свидетельствуют о том, что использование градиентного бустинга для решения этой проблемы имеет высокий потенциал. The purpose of the research of this article is a comparative analysis of algorithms based on gradient boosting, which can be applied to solving the problem of intrusion detection and mitigation of the consequences of an attack in Internet of Things systems. This article substantiates the importance of improving the security of Internet of Things systems, emphasizing the limitations of current measures due to the lack of resources of these devices. It is proposed to use machine learning algorithms based on gradient boosting to help detect network intrusions. An intrusion detection experiment was conducted using the following algorithms: Adaptive Boosting (ADB), Gradient Descent, Extreme Gradient Boosting (XGB), Categorical Boosting (CAB) and Light Gradient Boosting (LGB). The results obtained indicate that the use of gradient boosting to solve this problem has a high potential.

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Kriuchkova, Anastasiia, Varvara Toloknova, and Svitlana Drin. "Predictive model for a product without history using LightGBM. Pricing model for a new product." Mohyla Mathematical Journal 6 (April 18, 2024): 6–13. http://dx.doi.org/10.18523/2617-7080620236-13.

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The article focuses on developing a predictive product pricing model using LightGBM. Also, the goal was to adapt the LightGBM method for regression problems and, especially, in the problems of forecasting the price of a product without history, that is, with a cold start.The article contains the necessary concepts to understand the working principles of the light gradient boosting machine, such as decision trees, boosting, random forests, gradient descent, GBM (Gradient Boosting Machine), GBDT (Gradient Boosting Decision Trees). The article provides detailed insights into the algorithms used for identifying split points, with a focus on the histogram-based approach.LightGBM enhances the gradient boosting algorithm by introducing an automated feature selection mechanism and giving special attention to boosting instances characterized by more substantial gradients. This can lead to significantly faster training and improved prediction performance. The Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB) techniques used as enhancements to LightGBM are vividly described. The article presents the algorithms for both techniques and the complete LightGBM algorithm.This work contains an experimental result. To test the lightGBM, a real dataset of one Japanese C2C marketplace from the Kaggle site was taken. In the practical part, a performance comparison between LightGBM and XGBoost (Extreme Gradient Boosting Machine) was performed. As a result, only a slight increase in estimation performance (RMSE, MAE, R-squard) was found by applying LightGBM over XGBoost, however, there exists a notable contrast in the training procedure’s time efficiency. LightGBM exhibits an almost threefold increase in speed compared to XGBoost, making it a superior choice for handling extensive datasets.This article is dedicated to the development and implementation of machine learning models for product pricing using LightGBM. The incorporation of automatic feature selection, a focus on highgradient examples, and techniques like GOSS and EFB demonstrate the model’s versatility and efficiency. Such predictive models will help companies improve their pricing models for a new product. The speed of obtaining a forecast for each element of the database is extremely relevant at a time of rapid data accumulation.

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Dissertations / Theses on the topic "Gradient boosting"

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Werner, Tino [Verfasser], Peter [Akademischer Betreuer] Ruckdeschel, and Matthias [Akademischer Betreuer] Schmid. "Gradient-Free Gradient Boosting / Tino Werner ; Peter Ruckdeschel, Matthias Schmid." Oldenburg : BIS der Universität Oldenburg, 2020. http://d-nb.info/120419968X/34.

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Moreni, Matilde <1994&gt. "Prediction of Cryptocurrency prices using Gradient Boosting machine." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/17739.

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The Gradient Boosting is a machine learning approach that is widely used due to its high performance and accuracy. The aim of this thesis is find out how good is the performance of Gradient Boosting applied to the price forecasting of Cryptocurrencies and then to flat currencies. The thesis is developed in three sections, the first is an overview of the Cryptocurrencies 's world, the second is an explanation of how Decision trees works and a mayor focus on Gradient Boosting. The last section is the practical part, where there is the application of Gradient Boosting to the price forecasting of cryptocurrencies and then the application of the same algorithm to flat currencies. The aim is to find out if the performance of Gradient Boosting is better for cryptocurrencies forecasting or flat currencies.

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Mayr, Andreas [Verfasser]. "Boosting beyond the mean - extending component-wise gradient boosting algorithms to multiple dimensions / Andreas Mayr." München : Verlag Dr. Hut, 2013. http://d-nb.info/104287848X/34.

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Ahlgren, Marcus. "Claims Reserving using Gradient Boosting and Generalized Linear Models." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229406.

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One fundamental function of an insurance company revolves around calculating the expected claims costs for which the insurer has to compensate its policyholders for. This is the process of claims reserving which is practised by actuaries using statistical methods. Over the last few decades statistical learning methods have become increasingly popular due to their ability to find complex patterns in any type of data. However, they have not been widely adapted within the insurance sector. In this thesis we evaluate the capability of claims reserving with the method of gradient boosting, a non-parametric statistical learning method that has proven to be successful within multiple other disciplines which has made it very popular. The gradient boosting technique is compared with the generalized linear model(GLM) which is widely used for modelling claims. We compare the models by using a claims data set provided by Länsförsäkringar AB which allows us to train the models and evaluate their performance on data not yet seen by the models. The models were implemented using R. The results show that the GLM has a lower prediction error. Also, the gradient boosting method requires more fine tuning to handle claims data properly while the GLM already possesses certain features that makes it suitable for claims reserving without making as many adjustments in the model implementation. The advantage of capturing complex dependencies in data is not fully utilized in this thesis since we only work with 6 predictor variables. It is more likely that gradient boosting can compete with GLM when predicting more complicated claims.
En av de centrala verksamheterna ett försäkringsbolag arbetar med handlar om att uppskatta skadekostnader för att kunna ersätta försäkringstagarna. Denna procedur kallas reservsättning och utförs av aktuarier med hjälp av statistiska metoder. Under de senaste årtiondena har statistiska inlärningsmetoder blivit mer och mer populära tack vare deras förmåga att hitta komplexa mönster i alla typer av data. Dock har intresset för dessa varit relativt lågt inom försäkringsbranschen till förmån för mer traditionella försäkringsmatematiska metoder. I den här masteruppsatsen undersöker vi förmågan att reservsätta med metoden \textit{gradient boosting}, en icke-parametrisk statistisk inlärningsmetod som har visat sig fungera mycket väl inom en rad andra områden vilket har gjort metoden mycket populär. Vi jämför denna metod med generaliserade linjära modeller(GLM) som är en av de vanliga metoderna vid reservsättning. Vi jämför modellerna med hjälp av ett dataset tillhandahålls av Länsförsäkringar AB. Modellerna implementerades med R. 80\% av detta dataset används för att träna modellerna och resterande 20\% används för att evaluera modellernas prediktionsförmåga på okänd data. Resultaten visar att GLM har ett lägre prediktionsfel. Gradient boosting kräver att ett antal hyperparametrar justeras manuellt för att få en välfungerande modell medan GLM inte kräver lika mycket korrigeringar varför den är mer praktiskt lämpad. Fördelen med att kunna modellerna komplexa förhållanden i data utnyttjas inte till fullo i denna uppsats då vi endast arbetar med sex prediktionsvariabler. Det är sannolikt att gradient boosting skulle ge bättre resultat med mer komplicerade datastrukturer.

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Kriegler, Brian. "Cost-sensitive stochastic gradient boosting within a quantitative regression framework." Diss., Restricted to subscribing institutions, 2007. http://proquest.umi.com/pqdweb?did=1383476771&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Sabo, Juraj. "Gradient Boosting Machine and Artificial Neural Networks in R and H2O." Master's thesis, Vysoká škola ekonomická v Praze, 2016. http://www.nusl.cz/ntk/nusl-264614.

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Artificial neural networks are fascinating machine learning algorithms. They used to be considered unreliable and computationally very expensive. Now it is known that modern neural networks can be quite useful, but their computational expensiveness unfortunately remains. Statistical boosting is considered to be one of the most important machine learning ideas. It is based on an ensemble of weak models that together create a powerful learning system. The goal of this thesis is the comparison of these machine learning models on three use cases. The first use case deals with modeling the probability of burglary in the city of Chicago. The second use case is the typical example of customer churn prediction in telecommunication industry and the last use case is related to the problematic of the computer vision. The second goal of this thesis is to introduce an open-source machine learning platform called H2O. It includes, among other things, an interface for R and it is designed to run in standalone mode or on Hadoop. The thesis also includes the introduction into an open-source software library Apache Hadoop that allows for distributed processing of big data. Concretely into its open-source distribution Hortonworks Data Platform.

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Söderholm, Matilda. "Predicting Risk of Delays in Postal Deliveries with Neural Networks and Gradient Boosting Machines." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-169478.

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This thesis conducts a study on a data set from the Swedish and Danish postal service Postnord, comparing an artificial neural network (ANN) and a gradient boosting machine (GBM) for predicting delays in package deliveries. The models are evaluated based on F1-score for the important class which represents the data points that are delayed and needed to be identified. The GBM is already implemented and tuned using grid search by Postnord, the ANN is tuned using sequential model based optimization with the tree Parzen estimator function. Furthermore, it is trained using dynamic resampling to handle the imbalanced data set. Even with several measures implemented to handle the class imbalance, the ANN performs poorly when tested on unseen data, unlike the GBM. The GBM has high precision (84%) and decent recall (24%), which produces a F1-score of 0.38. The ANN has high recall (62%) but extremely low precision (5%) which gives a F1-score of 0.08, indicating that it is biased to predict sample as delayed when it is in time. The GBM has a natural handling of class imbalance unlike the ANN, and even with measures taken to improve the ANN and its handling of class imbalance, GBM performs better.

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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 criticism. As a result, numerous cost-sensitive modifications of the original algorithm have been proposed. Each of these has its own motivations, and its own claims to superiority. With a thorough analysis of the literature 1997-2016, we find 15 distinct cost-sensitive Boosting variants - discounting minor variations. We critique the literature using {\em four} powerful theoretical frameworks: Bayesian decision theory, the functional gradient descent view, margin theory, and probabilistic modelling. From each framework, we derive a set of properties which must be obeyed by boosting algorithms. We find that only 3 of the published Adaboost variants are consistent with the rules of all the frameworks - and even they require their outputs to be calibrated to achieve this. Experiments on 18 datasets, across 21 degrees of cost asymmetry, all support the hypothesis - showing that once calibrated, the three variants perform equivalently, outperforming all others. Our final recommendation - based on theoretical soundness, simplicity, flexibility and performance - is to use the original Adaboost algorithm albeit with a shifted decision threshold and calibrated probability estimates. The conclusion is that novel cost-sensitive boosting algorithms are unnecessary if proper calibration is applied to the original.

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Mayrink, Victor Teixeira de Melo. "Avaliação do algoritmo Gradient Boosting em aplicações de previsão de carga elétrica a curto prazo." Universidade Federal de Juiz de Fora (UFJF), 2016. https://repositorio.ufjf.br/jspui/handle/ufjf/3563.

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FAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerais
O armazenamento de energia elétrica em larga escala ainda não é viável devido a restrições técnicas e econômicas. Portanto, toda energia consumida deve ser produzida instantaneamente; não é possível armazenar o excesso de produção, ou tampouco cobrir eventuais faltas de oferta com estoques de segurança, mesmo que por um curto período de tempo. Consequentemente, um dos principais desafios do planejamento energético consiste em realizar previsões acuradas para as demandas futuras. Neste trabalho, apresentamos um modelo de previsão para o consumo de energia elétrica a curto prazo. A metodologia utilizada compreende a construção de um comitê de previsão, por meio da aplicação do algoritmo Gradient Boosting em combinação com modelos de árvores de decisão e a técnica de amortecimento exponencial. Esta estratégia compreende um método de aprendizado supervisionado que ajusta o modelo de previsão com base em dados históricos do consumo de energia, das temperaturas registradas e de variáveis de calendário. Os modelos propostos foram testados em duas bases de dados distintas e demonstraram um ótimo desempenho quando comparados com resultados publicados em outros trabalhos recentes.
The storage of electrical energy is still not feasible on a large scale due to technical and economic issues. Therefore, all energy to be consumed must be produced instantly; it is not possible to store the production leftover, or either to cover any supply shortages with safety stocks, even for a short period of time. Thus, one of the main challenges of energy planning consists in computing accurate forecasts for the future demand. In this paper, we present a model for short-term load forecasting. The methodology consists in composing a prediction comitee by applying the Gradient Boosting algorithm in combination with decision tree models and the exponential smoothing technique. This strategy comprises a supervised learning method that adjusts the forecasting model based on historical energy consumption data, the recorded temperatures and calendar variables. The proposed models were tested in two di erent datasets and showed a good performance when compared with results published in recent papers.

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Sjöblom, Niklas. "Evolutionary algorithms in statistical learning : Automating the optimization procedure." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160118.

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Scania has been working with statistics for a long time but has invested in becoming a data driven company more recently and uses data science in almost all business functions. The algorithms developed by the data scientists need to be optimized to be fully utilized and traditionally this is a manual and time consuming process. What this thesis investigates is if and how well evolutionary algorithms can be used to automate the optimization process. The evaluation was done by implementing and analyzing four variations of genetic algorithms with different levels of complexity and tuning parameters. The algorithm subject to optimization was XGBoost, a gradient boosted tree model, applied to data that had previously been modelled in a competition. The results show that evolutionary algorithms are applicable in finding good models but also emphasizes the importance of proper data preparation.
Scania har länge jobbat med statistik men har på senare år investerat i att bli ett mer datadrivet företag och använder nu data science i nästan alla avdelningar på företaget. De algoritmer som utvecklas av data scientists måste optimeras för att kunna utnyttjas till fullo och detta är traditionellt sett en manuell och tidskrävade process. Detta examensarbete utreder om och hur väl evolutionära algoritmer kan användas för att automatisera optimeringsprocessen. Utvärderingen gjordes genom att implementera och analysera fyra varianter avgenetiska algoritmer med olika grader av komplexitet och trimningsparameterar. Algoritmen som var målet för optimering var XGBoost, som är en gradient boosted trädbaserad modell. Denna applicerades på data som tidigare hade modellerats i entävling. Resultatet visar att evolutionära algoritmer är applicerbara i att hitta bra modellermen påvisar även hur fundamentalt det är att arbeta med databearbetning innan modellering.

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Books on the topic "Gradient boosting"

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Jome, Kaitlyn. Gradient Boosting Trees : a Beginner's Guide for Gradient Boosting: Decision Tree Machine Learning Projects. Independently Published, 2021.

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Nylen, Jonas. Gradient Boosting Trees 101 : How Do Decision Trees Work: Boosting. Independently Published, 2021.

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Verhagen, Efren. Gradient Boosting Trees 101 : How Do Decision Trees Work: Gradient Boosted Trees. Independently Published, 2021.

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Xgboost. the Extreme Gradient Boosting for Mining Applications. GRIN Verlag GmbH, 2018.

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Hands-On Gradient Boosting with XGBoost and Scikit-learn: Perform Accessible Machine Learning and Extreme Gradient Boosting with Python. Packt Publishing, Limited, 2020.

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Bhave, Roshan. Practical Machine Learning with LightGBM and Python: Explore Microsofts Gradient Boosting Framework to Optimize Machine Learning. Packt Publishing, Limited, 2021.

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Decision Making Handbook: Career Readiness, Bank Receptionist, Vector Concept, Crypto Wallets, Forrester Wave,TR6 Internet Entrepreneur, Bvnk, Financial Position, Gradient Boosting. Independently Published, 2022.

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Book chapters on the topic "Gradient boosting"

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Truong, Dothang. "Gradient Boosting." In Data Science and Machine Learning for Non-Programmers, 479–504. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003162872-18.

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Ayyadevara, V. Kishore. "Gradient Boosting Machine." In Pro Machine Learning Algorithms, 117–34. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3564-5_6.

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Greenwell, Brandon M. "Gradient boosting machines." In Tree-Based Methods for Statistical Learning in R, 309–58. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003089032-8.

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Biau, Gérard, and Benoît Cadre. "Optimization by Gradient Boosting." In Advances in Contemporary Statistics and Econometrics, 23–44. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73249-3_2.

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Emami, Seyedsaman, Carlos Ruiz Pastor, and Gonzalo Martínez-Muñoz. "Multi-Task Gradient Boosting." In Lecture Notes in Computer Science, 97–107. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40725-3_9.

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Denuit, Michel, Donatien Hainaut, and Julien Trufin. "Gradient Boosting with Neural Networks." In Springer Actuarial, 167–92. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25827-6_7.

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Knieper, Lars, Thomas Kneib, and Elisabeth Bergherr. "Spatial Confounding in Gradient Boosting." In Contributions to Statistics, 88–94. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65723-8_14.

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Nziyumva, Eric, Rong Hu, Chih-Yu Hsu, and Jovial Niyogisubizo. "Electrical Load Forecasting Using Hybrid of Extreme Gradient Boosting and Light Gradient Boosting Machine." In Lecture Notes in Electrical Engineering, 1083–93. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6963-7_95.

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Wan, Lunjun, Ke Tang, and Rui Wang. "Gradient Boosting-Based Negative Correlation Learning." In Intelligent Data Engineering and Automated Learning – IDEAL 2013, 358–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41278-3_44.

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Tahir, Muhammad. "Brain MRI Classification Using Gradient Boosting." In Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology, 294–301. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66843-3_29.

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Conference papers on the topic "Gradient boosting"

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Wang, Hongfei, Chenliang Luo, Deqing Zou, Hai Jin, and Wenjie Cai. "Gradient Boosting-Accelerated Evolution for Multiple-Fault Diagnosis." In 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE), 1–6. IEEE, 2024. http://dx.doi.org/10.23919/date58400.2024.10546656.

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Diego, Ferran, and Fred A. Hamprecht. "Structured Regression Gradient Boosting." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.162.

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Cheng, Chen, Fen Xia, Tong Zhang, Irwin King, and Michael R. Lyu. "Gradient boosting factorization machines." In the 8th ACM Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2645710.2645730.

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Timmons, Caitlin, Andrea Boskovic, Sreeharsha Lakamsani, Walter Gerych, Luke Buquicchio, and Elke Rundensteiner. "Positive Unlabeled Gradient Boosting." In 2020 IEEE MIT Undergraduate Research Technology Conference (URTC). IEEE, 2020. http://dx.doi.org/10.1109/urtc51696.2020.9668901.

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Grari, Vincent, Boris Ruf, Sylvain Lamprier, and Marcin Detyniecki. "Fair Adversarial Gradient Tree Boosting." In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 2019. http://dx.doi.org/10.1109/icdm.2019.00124.

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Enkhtaivan, Batnyam, and Isamu Teranishi. "pGBF: Personalized Gradient Boosting Forest." In 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, 2023. http://dx.doi.org/10.1109/ijcnn54540.2023.10191289.

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A, Ashwini, and Loganathan V. "Nutrigrow Using Gradient Boosting Regressor." In 2024 1st International Conference on Trends in Engineering Systems and Technologies (ICTEST). IEEE, 2024. http://dx.doi.org/10.1109/ictest60614.2024.10576157.

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Ibragimov, Bulat, and Anton Vakhrushev. "Uplift Modelling via Gradient Boosting." In KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 1177–87. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3637528.3672019.

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Liu, Yang, Zhuo Ma, Ximeng Liu, Siqi Ma, Surya Nepal, Robert H. Deng, and Kui Ren. "Boosting Privately: Federated Extreme Gradient Boosting for Mobile Crowdsensing." In 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2020. http://dx.doi.org/10.1109/icdcs47774.2020.00017.

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Ying, Bicheng, and Ali H. Sayed. "Diffusion gradient boosting for networked learning." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952609.

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Reports on the topic "Gradient boosting"

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Rossi, Jose Luiz, Carlos Piccioni, Marina Rossi, and Daniel Cuajeiro. Brazilian Exchange Rate Forecasting in High Frequency. Inter-American Development Bank, September 2022. http://dx.doi.org/10.18235/0004488.

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We investigated the predictability of the Brazilian exchange rate at High Frequency (1, 5 and 15 minutes), using local and global economic variables as predictors. In addition to the Linear Regression method, we use Machine Learning algorithms such as Ridge, Lasso, Elastic Net, Random Forest and Gradient Boosting. When considering contemporary predictors, it is possible to outperform the Random Walk at all frequencies, with local economic variables having greater predictive power than global ones. Machine Learning methods are also capable of reducing the mean squared error. When we consider only lagged predictors, it is possible to beat the Random Walk if we also consider the Brazilian Real futures as an additional predictor, for the frequency of one minute and up to two minutes ahead, confirming the importance of the Brazilian futures market in determining the spot exchange rate.

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Forteza, Nicolás, and Sandra García-Uribe. A Score Function to Prioritize Editing in Household Survey Data: A Machine Learning Approach. Madrid: Banco de España, October 2023. http://dx.doi.org/10.53479/34613.

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Errors in the collection of household finance survey data may proliferate in population estimates, especially when there is oversampling of some population groups. Manual case-by-case revision has been commonly applied in order to identify and correct potential errors and omissions such as omitted or misreported assets, income and debts. We derive a machine learning approach for the purpose of classifying survey data affected by severe errors and omissions in the revision phase. Using data from the Spanish Survey of Household Finances we provide the best-performing supervised classification algorithm for the task of prioritizing cases with substantial errors and omissions. Our results show that a Gradient Boosting Trees classifier outperforms several competing classifiers. We also provide a framework that takes into account the trade-off between precision and recall in the survey agency in order to select the optimal classification threshold.

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Liu, Hongrui, and Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, November 2021. http://dx.doi.org/10.31979/mti.2021.2102.

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In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accidents data consisting of 49 variables describing 4.2 million accident records from February 2016 to December 2020, as well as logistic regression, tree-based techniques such as Decision Tree Classifier and Random Forest Classifier (RF), and Extreme Gradient boosting (XG-boost) to process and train the models. These models will assist people in making smart real-time transportation decisions to improve mobility and reduce accidents.

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Jääskeläinen, Emmihenna. Construction of reliable albedo time series. Finnish Meteorological Institute, September 2023. http://dx.doi.org/10.35614/isbn.9789523361782.

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A reliable satellite-based black-sky albedo time series is a crucial part of detecting changes in the climate. This thesis studies the solutions to several uncertainties impairing the quality of the black-sky albedo time series. These solutions include creating a long dynamic aerosol optical depth time series for enhancing the removal of atmospheric effects, a method to fill missing data to improve spatial and temporal coverage, and creating a function to correctly model the diurnal variation of melting snow albedo. Mathematical methods are the center pieces of the solutions found in this thesis. Creating a melting snow albedo function and the construction of an aerosol optical depth time series lean on a linear regression approach, whereas the process to fill missing values is based on gradient boosting, a machine learning method that is in turn based on decision trees. These methods reflect the basic nature of these problems as well as the need to take into account the large amounts of satellite-based data and computational resources available.

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