Relevant bibliographies by topics / ENSEMBLE LEARNING MODELS

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'ENSEMBLE LEARNING MODELS'

Author: Grafiati
Published: 11 September 2023
Last updated: 26 July 2025

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Journal articles on the topic "ENSEMBLE LEARNING MODELS"

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Imran, Sheik, and Pradeep N. "A Review on Ensemble Machine and Deep Learning Techniques Used in the Classification of Computed Tomography Medical Images." International Journal of Health Sciences and Research 14, no. 1 (2024): 201–13. http://dx.doi.org/10.52403/ijhsr.20240124.

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Ensemble learning combines multiple base models to enhance predictive performance and generalize better on unseen data. In the context of Computed Tomography (CT) image processing, ensemble techniques often leverage diverse machine learning or deep learning architectures to achieve the best results. Ensemble machine learning and deep learning techniques have revolutionized the field of CT image processing by significantly improving accuracy, robustness, and efficiency in various medical imaging tasks. These methods have been instrumental in tasks such as image reconstruction, segmentation, cla
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GURBYCH, A. "METHOD SUPER LEARNING FOR DETERMINATION OF MOLECULAR RELATIONSHIP." Herald of Khmelnytskyi National University. Technical sciences 307, no. 2 (2022): 14–24. http://dx.doi.org/10.31891/2307-5732-2022-307-2-14-24.

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This paper uses the Super Learning principle to predict the molecular affinity between the receptor (large biomolecule) and ligands (small organic molecules). Meta-models study the optimal combination of individual basic models in two consecutive ensembles – classification and regression. Each costume contains six models of machine learning, which are combined by stacking. Base models include the reference vector method, random forest, gradient boosting, neural graph networks, direct propagation, and transformers. The first ensemble predicts binding probability and classifies all candidate mol
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Saqib, Malik, and Sharma Narendra. "A Vast Review of Recognizing the Presence of Android Malware Based on Ensemble Machine Learning Technique." Indian Journal of Science and Technology 17, no. 2 (2024): 149–65. https://doi.org/10.17485/IJST/v17i2.2406.

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Abstract <strong>Background:</strong>&nbsp;It is evaluated that there is 70% to 80% of smartphone users have an Android mobile. Given its trend, a lot of malware strikes on the Android OS. In 2018, the largest number of malware attacks was identified, when there were 10.5 billion such malicious activity detected worldwide. Machine learning has emerged as a promising approach for detecting Android malware, and Ensemble machine learning has been shown to enhance the accuracy of malware detection in other domains. Objectives: In this paper, the systematic literature review were conducted using na
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ACOSTA-MENDOZA, NIUSVEL, ALICIA MORALES-REYES, HUGO JAIR ESCALANTE, and ANDRÉS GAGO-ALONSO. "LEARNING TO ASSEMBLE CLASSIFIERS VIA GENETIC PROGRAMMING." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 07 (2014): 1460005. http://dx.doi.org/10.1142/s0218001414600052.

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This paper introduces a novel approach for building heterogeneous ensembles based on genetic programming (GP). Ensemble learning is a paradigm that aims at combining individual classifier's outputs to improve their performance. Commonly, classifiers outputs are combined by a weighted sum or a voting strategy. However, linear fusion functions may not effectively exploit individual models' redundancy and diversity. In this research, a GP-based approach to learn fusion functions that combine classifiers outputs is proposed. Heterogeneous ensembles are aimed in this study, these models use individ
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d, d., d. d, d. d, and d. d. "Optimized Deep Learning Models Using Ensemble Learning for COVID-19 Detection on CT Scan Images." Korean Data Analysis Society 25, no. 6 (2023): 2027–39. http://dx.doi.org/10.37727/jkdas.2023.25.6.2027.

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Early identification of COVID-19 can facilitate the establishment of a swift medical response plan, thereby slowing the rapid dissemination of this deadly disease. Recent advancements in medical imaging technology, coupled with the successful application of deep learning to visual tasks, have driven numerous studies investigating early disease diagnosis through medical imaging. In particular, deep learning has been employed for COVID-19 diagnosis from CT scan images. This paper proposes an ensemble COVID detection model that integrates four models including GoogleNet, EfficientNet, Hybrid Effi
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Siswoyo, Bambang, Zuraida Abal Abas, Ahmad Naim Che Pee, Rita Komalasari, and Nano Suryana. "Ensemble machine learning algorithm optimization of bankruptcy prediction of bank." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 679. http://dx.doi.org/10.11591/ijai.v11.i2.pp679-686.

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The ensemble consists of a single set of individually trained models, the predictions of which are combined when classifying new cases, in building a good classification model requires the diversity of a single model. The algorithm, logistic regression, support vector machine, random forest, and neural network are single models as alternative sources of diversity information. Previous research has shown that ensembles are more accurate than single models. Single model and modified ensemble bagging model are some of the techniques we will study in this paper. We experimented with the banking in
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Bambang, Siswoyo, Abal Abas Zuraida, Naim Che Pee Ahmad, Komalasari Rita, and Suyatna Nano. "Ensemble machine learning algorithm optimization of bankruptcy prediction of bank." International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 679–86. https://doi.org/10.11591/ijai.v11.i2.pp679-686.

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The ensemble consists of a single set of individually trained models, the predictions of which are combined when classifying new cases, in building a good classification model requires the diversity of a single model. The algorithm, logistic regression, support vector machine, random forest, and neural network are single models as alternative sources of diversity information. Previous research has shown that ensembles are more accurate than single models. Single model and modified ensemble bagging model are some of the techniques we will study in this paper. We experimented with the banking in
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Matushkin, Dmytro. "PHOTOVOLTAIC GENERATION FORECASTING MODELS: CONCEPTUAL ENSEMBLE ARCHITECTURES." System Research in Energy 2024, no. 4 (2024): 56–64. https://doi.org/10.15407/srenergy2024.04.056.

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The decisions regarding power regulation, energy resource planning, and integrating “green” energy into the electrical grid hinge on precise probabilistic forecasts. One of the potential strategies to enhance forecast accuracy is the utilization of ensemble forecasting methods. They represent an approach where multiple models collaborate to achieve superior results compared to what a single model could produce independently. These methods can be categorized into two main categories: competitive and collaborative ensembles. Competitive ensembles harness the diversity of parameters and data to c
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Huang, Haifeng, Lei Huang, Rongjia Song, Feng Jiao, and Tao Ai. "Bus Single-Trip Time Prediction Based on Ensemble Learning." Computational Intelligence and Neuroscience 2022 (August 11, 2022): 1–24. http://dx.doi.org/10.1155/2022/6831167.

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The prediction of bus single-trip time is essential for passenger travel decision-making and bus scheduling. Since many factors could influence bus operations, the accurate prediction of the bus single-trip time faces a great challenge. Moreover, bus single-trip time has obvious nonlinear and seasonal characteristics. Hence, in order to improve the accuracy of bus single-trip time prediction, five prediction algorithms including LSTM (Long Short-term Memory), LR (Linear Regression), KNN (K-Nearest Neighbor), XGBoost (Extreme Gradient Boosting), and GRU (Gate Recurrent Unit) are used and examin
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Zhang, Yonglin, Lezheng Yu, Li Xue, Fengjuan Liu, Runyu Jing, and Jiesi Luo. "Optimizing lipocalin sequence classification with ensemble deep learning models." PLOS ONE 20, no. 4 (2025): e0319329. https://doi.org/10.1371/journal.pone.0319329.

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Deep learning (DL) has become a powerful tool for the recognition and classification of biological sequences. However, conventional single-architecture models often struggle with suboptimal predictive performance and high computational costs. To address these challenges, we present EnsembleDL-Lipo, an innovative ensemble deep learning framework that combines Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) to enhance the identification of lipocalin sequences. Lipocalins are multifunctional extracellular proteins involved in various diseases and stress responses, and their l
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Dissertations / Theses on the topic "ENSEMBLE LEARNING MODELS"

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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 apparaît 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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Books on the topic "ENSEMBLE LEARNING MODELS"

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Kyriakides, George, and Konstantinos G. Margaritis. Hands-On Ensemble Learning with Python: Build Highly Optimized Ensemble Machine Learning Models Using Scikit-Learn and Keras. Packt Publishing, Limited, 2019.

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Benatan, Matt, Jochem Gietema, and Marian Schneider. Bayesian Deep Learning: Work with Bayesian Neural Networks BNN and BDL to Employ an Ensemble of Deep Learning Models. Packt Publishing, Limited, 2023.

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Head, Paul D. The Choral Experience. Edited by Frank Abrahams and Paul D. Head. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199373369.013.3.

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Much has changed in the choral rehearsal room over the past two generations, particularly in regard to the role the choral conductor assumes—or commands—in the rehearsal process. This chapter discusses the ever-evolving stereotypical roles of the conductor, while examining alternatives to traditional leadership models with particular emphasis on the encouragement of student engagement and peer-based learning. In addition to the facilitation of collaborative learning exercises, the chapter outlines a specific process of written interaction with the choral ensemble. This section is inspired by t
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Summerson, Samantha R., and Caleb Kemere. Multi-electrode Recording of Neural Activity in Awake Behaving Animals. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199939800.003.0004.

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Systems neuroscience is being revolutionized by the ability to record the activity of large numbers of neurons simultaneously. Chronic recording with multi- electrode arrays in animal models is a critical tool for studies of learning and memory, sensory processing, motor control, emotion, and decision-making. The experimental process for gathering large amounts of neural ensemble data can be very time consuming, however, the resulting data can be incredibly rich. We present a detailed overview of the process of acquiring multichannel neural data, with a particular focus on chronic tetrode reco
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Wheelahan, Leesa. Rethinking Skills Development. Edited by John Buchanan, David Finegold, Ken Mayhew, and Chris Warhurst. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199655366.013.30.

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This article critiques models of competency-based training in vocational education and training in Anglophone countries and contrasts it to ‘kompetenz’ in Germanic countries. It identifies six key problems with Competency-Based Training (CBT): first, CBT is tied to specific ensembles of workplace roles and requirements; second, the outcomes of learning are tied to descriptions of work as it currently exists; third, CBT does not provide adequate access to underpinning knowledge; fourth, CBT is based on the simplistic and behaviourist notion that processes of learning are identical with the skil
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Rodrigues, Valerian. Ambedkar's Political Philosophy. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/9780198925422.001.0001.

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Abstract This study is organized around a set of key concepts that Ambedkar, the Indian thinker and leader of the socially marginalized, proposed to reconstruct public life, factoring in oppression and degradation. This framework conceived human beings as endowed with a distinct set of attributes entitling them to consideration as moral equals despite other differences among them. It also accorded a procedural priority to consciousness in human understanding. Ambedkar deployed this framework to contend against social institutions of caste, untouchability, and other forms of marginalities and t
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Book chapters on the topic "ENSEMBLE LEARNING MODELS"

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Coqueret, Guillaume, and Tony Guida. "Ensemble models." In Machine Learning for Factor Investing. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003121596-14.

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Kumar, Alok, and Mayank Jain. "Mixing Models." In Ensemble Learning for AI Developers. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5940-5_3.

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Bisong, Ekaba. "Ensemble Methods." In Building Machine Learning and Deep Learning Models on Google Cloud Platform. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4470-8_23.

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Hennicker, Rolf, Alexander Knapp, and Martin Wirsing. "Epistemic Ensembles." In Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19759-8_8.

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AbstractAn ensemble consists of a set of computing entities which collaborate to reach common goals. We introduce epistemic ensembles that use shared knowledge for collaboration between agents. Collaboration is achieved by different kinds of knowledge announcements. For specifying epistemic ensemble behaviours we use formulas of dynamic logic with compound ensemble actions. Our semantics relies on an epistemic notion of ensemble transition systems as behavioural models. These transition systems describe control flow over epistemic states for expressing knowledge-based collaboration of agents.
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Juniper, Matthew P. "Machine Learning for Thermoacoustics." In Lecture Notes in Energy. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-16248-0_11.

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AbstractThis chapter demonstrates three promising ways to combine machine learning with physics-based modelling in order to model, forecast, and avoid thermoacoustic instability. The first method assimilates experimental data into candidate physics-based models and is demonstrated on a Rijke tube. This uses Bayesian inference to select the most likely model. This turns qualitatively-accurate models into quantitatively-accurate models that can extrapolate, which can be combined powerfully with automated design. The second method assimilates experimental data into level set numerical simulations
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Brazdil, Pavel, Jan N. van Rijn, Carlos Soares, and Joaquin Vanschoren. "Metalearning in Ensemble Methods." In Metalearning. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-67024-5_10.

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AbstractThis chapter discusses some approaches that exploit metalearning methods in ensemble learning. It starts by presenting a set of issues, such as the ensemble method used, which affect the process of ensemble learning and the resulting ensemble. In this chapter we discuss various lines of research that were followed. Some approaches seek an ensemble-based solution for the whole dataset, others for individual instances. Regarding the first group, we focus on metalearning in the construction, pruning and integration phase. Modeling the interdependence of models plays an important part in t
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Singh, Devanshi, Ahmad Habib Khan, and Shweta Meena. "Fake News Detection Using Ensemble Learning Models." In Proceedings of Data Analytics and Management. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6553-3_4.

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Deon, Samara, José Donizetti de Lima, Geremi Gilson Dranka, et al. "Ensemble Learning Models for Wind Power Forecasting." In Advances in Intelligent Systems and Computing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-66635-3_2.

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Li, Weishan. "Developed Ensemble Model Based on Multiple Machine Learning Models." In Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023). Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-300-9_69.

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Petluru, Surya, and Pradeep Singh. "Facial Expression Recognition Using Ensemble Learning of Transfer Learning Models." In Studies in Autonomic, Data-driven and Industrial Computing. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-5435-3_39.

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Conference papers on the topic "ENSEMBLE LEARNING MODELS"

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Wang, Xiyue. "Ensemble Learning Based Models for Planet Classification." In International Conference on Innovations in Applied Mathematics, Physics and Astronomy. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012992000004601.

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Gonuguntla, Sai Dedipya, Sunkavalli JayaPrakash, and Rayudu Harshith Sai. "Intrusion Detection Using Ensemble Machine Learning Models." In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI). IEEE, 2025. https://doi.org/10.1109/icmsci62561.2025.10894278.

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Sakib, Md Nazmus, Md Alif Sheakh, Mst Sazia Tahosin, Md Rezwane Sadik, Md Amirul Islam, and Lima Akter. "Accurate Thyroid Disease Detection with Ensemble Learning Models." In 2024 4th International Conference on Artificial Intelligence and Signal Processing (AISP). IEEE, 2024. https://doi.org/10.1109/aisp61711.2024.10870726.

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Sukhavasi, Vidyullatha, AP Chaitanyasri Mouli, Dev Vikas Juneja, Yadala Sucharitha, Sumedh Sameer Joshi, and Samir Dey. "Establishing Ensemble Learning Models for Daily Rainfall Forecasting." In 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63760.2024.10910429.

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Baliyan, Himanshu, and A. Rama Prasath. "Enhancing Phishing Website Detection Using Ensemble Machine Learning Models." In 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0. IEEE, 2024. http://dx.doi.org/10.1109/otcon60325.2024.10687754.

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Da Silva, Charles M. R., Paulo André L. De Castro, and Cecilia De A. C. Cesar. "Ransomware Detection: Ensemble Machine Learning Models Using Disjoint Data." In 2024 IEEE International Conference on Cyber Security and Resilience (CSR). IEEE, 2024. http://dx.doi.org/10.1109/csr61664.2024.10679469.

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Singh, Anuj Kumar, Amit Shukla, and Kirti Shukla. "Enhanced Lung Nodule Classification through Ensemble Transfer Learning Models." In 2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET). IEEE, 2024. http://dx.doi.org/10.1109/acet61898.2024.10730494.

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Waskita, A. A., Julfa Muhammad Amda, and Dwi Seno Kuncoro Sihono. "Enhancing Lung Cancer Classification with Ensemble Deep Learning Models." In 2024 International Conference on Computer, Control, Informatics and its Applications (IC3INA). IEEE, 2024. http://dx.doi.org/10.1109/ic3ina64086.2024.10732035.

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Alvina, Akimun Jannat, Yao Ma, and Mark Golkowski. "WLAN Protocols Identification Using Machine Learning and Ensemble Models." In 2025 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM). IEEE, 2025. https://doi.org/10.23919/usnc-ursinrsm66067.2025.10906840.

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Haque, Sayed Mahmudul, Md Zahidul Islam, Touhida Sultana Ety, Md Amir Hamja, Kanij Fatema, and Mahmudul Hasan. "Interpretable Blending Ensemble Learning Models for Cardiovascular Disease Prediction." In 2024 International Conference on Recent Progresses in Science, Engineering and Technology (ICRPSET). IEEE, 2024. https://doi.org/10.1109/icrpset64863.2024.10955903.

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Reports on the topic "ENSEMBLE LEARNING MODELS"

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de Luis, Mercedes, Emilio Rodríguez, and Diego Torres. Machine learning applied to active fixed-income portfolio management: a Lasso logit approach. Banco de España, 2023. http://dx.doi.org/10.53479/33560.

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The use of quantitative methods constitutes a standard component of the institutional investors’ portfolio management toolkit. In the last decade, several empirical studies have employed probabilistic or classification models to predict stock market excess returns, model bond ratings and default probabilities, as well as to forecast yield curves. To the authors’ knowledge, little research exists into their application to active fixed-income management. This paper contributes to filling this gap by comparing a machine learning algorithm, the Lasso logit regression, with a passive (buy-and-hold)
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Hart, Carl R., D. Keith Wilson, Chris L. Pettit, and Edward T. Nykaza. Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence. U.S. Army Engineer Research and Development Center, 2021. http://dx.doi.org/10.21079/11681/41182.

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Conventional numerical methods can capture the inherent variability of long-range outdoor sound propagation. However, computational memory and time requirements are high. In contrast, machine-learning models provide very fast predictions. This comes by learning from experimental observations or surrogate data. Yet, it is unknown what type of surrogate data is most suitable for machine-learning. This study used a Crank-Nicholson parabolic equation (CNPE) for generating the surrogate data. The CNPE input data were sampled by the Latin hypercube technique. Two separate datasets comprised 5000 sam
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Lasko, Kristofer, and Elena Sava. Semi-automated land cover mapping using an ensemble of support vector machines with moderate resolution imagery integrated into a custom decision support tool. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42402.

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Land cover type is a fundamental remote sensing-derived variable for terrain analysis and environmental mapping applications. The currently available products are produced only for a single season or a specific year. Some of these products have a coarse resolution and quickly become outdated, as land cover type can undergo significant change over a short time period. In order to enable on-demand generation of timely and accurate land cover type products, we developed a sensor-agnostic framework leveraging pre-trained machine learning models. We also generated land cover models for Sentinel-2 (
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Pettit, Chris, and D. Wilson. A physics-informed neural network for sound propagation in the atmospheric boundary layer. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41034.

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We describe what we believe is the first effort to develop a physics-informed neural network (PINN) to predict sound propagation through the atmospheric boundary layer. PINN is a recent innovation in the application of deep learning to simulate physics. The motivation is to combine the strengths of data-driven models and physics models, thereby producing a regularized surrogate model using less data than a purely data-driven model. In a PINN, the data-driven loss function is augmented with penalty terms for deviations from the underlying physics, e.g., a governing equation or a boundary condit
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Perdigão, Rui A. P., and Julia Hall. Empowering Next-Generation Synergies among Models and Data with Information Physical Quantum Technological Intelligence. Synergistic Manifolds, 2024. https://doi.org/10.46337/241209.

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We present and deploy our methodological and technological framework of Information Physical Quantum Technological Intelligence (IPQuTI), to empower next-generation mathematically robust, physically consistent, computationally efficient and operationally scalable synergies among models and data across multisectoral theoretical and applied workflows. Going beyond digital computing platforms, IPQuTI encompasses a richer basis alphabet of fundamental quantum states (information building blocks) and a high-order set of superposition and entanglement functionals (grammar) beyond the state of the ar
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Pedersen, Gjertrud. Symphonies Reframed. Norges Musikkhøgskole, 2018. http://dx.doi.org/10.22501/nmh-ar.481294.

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Symphonies Reframed recreates symphonies as chamber music. The project aims to capture the features that are unique for chamber music, at the juncture between the “soloistic small” and the “orchestral large”. A new ensemble model, the “triharmonic ensemble” with 7-9 musicians, has been created to serve this purpose. By choosing this size range, we are looking to facilitate group interplay without the need of a conductor. We also want to facilitate a richness of sound colours by involving piano, strings and winds. The exact combination of instruments is chosen in accordance with the features of
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Zhang, Caiyun, David Brodylo, Mizanur Rahman, Md Atiqur Rahman, Thomas Douglas, and Xavier Comas. Using an object-based machine learning ensemble approach to upscale evapotranspiration measured from eddy covariance towers in a subtropical wetland. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48056.

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Accurate prediction of evapotranspiration (ET) in wetlands is critical for understanding the coupling effects of water, carbon, and energy cycles in terrestrial ecosystems. Multiple years of eddy covariance (EC) tower ET measurements at five representative wetland ecosystems in the subtropical Big Cypress National Preserve (BCNP), Florida (USA) provide a unique opportunity to assess the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) ET operational product MOD16A2 and upscale tower measured ET to generate local/regional wetland ET maps. We developed an object-based mac
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Maher, Nicola, Pedro DiNezio, Antonietta Capotondi, and Jennifer Kay. Identifying precursors of daily to seasonal hydrological extremes over the USA using deep learning techniques and climate model ensembles. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769719.

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Douglas, Thomas, and Caiyun Zhang. Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41222.

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The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. V
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