Relevant bibliographies by topics / Advanced Ensemble Learning

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

Academic literature on the topic 'Advanced Ensemble Learning'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Advanced Ensemble Learning"

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Yaganteeswarudu, Akkem, Saroj Kumar Biswas, and Varanasi Aruna. "Streamlit Application for Advanced Ensemble Learning Methods in Crop Recommendation Systems – A Review and Implementation." Indian Journal of Science and Technology 16, no. 48 (2023): 4688–702. https://doi.org/10.17485/IJST/v16i48.2850.

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Abstract <strong>Objectives:</strong>&nbsp;This article explores the integration of advanced ensemble machine learning methods within precision agriculture, aiming to enhance the reliability and practical utility of crop recommendation systems. The incorporation of the Streamlit framework in the development process underpins our objective to deliver a user-friendly tool that provides farmers and agricultural analysts with actionable insights.&nbsp;<strong>Methods:</strong>&nbsp;A thorough literature review of artificial intelligence applications in agriculture serves as the foundation of our s
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Adamu, Yusuf Aliyu. "MALARIA PREDICTION MODEL USING ADVANCED ENSEMBLE MACHINE LEARNING TECHNIQUES." Journal of Medical pharmaceutical and allied sciences 10, no. 6 (2021): 3794–801. http://dx.doi.org/10.22270/jmpas.v10i6.1701.

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Malaria is a life-threatening disease that leads to death globally, its early prediction is necessary for preventing the rapid transmission. In this work, an enhanced ensemble learning approach for predicting malaria outbreaks is suggested. Using a mean-based splitting strategy, the dataset is randomly partitioned into smaller groups. The splits are then modelled using a classification and regression tree, and an accuracy-based weighted aging classifier ensemble is used to construct a homogenous ensemble from the several Classification and Regression Tree models. This approach ensures higher p
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Nandhini, A. Sunitha, J. Balakrishna, R. Bala Manikandan, and S. Bharath Kumar. "Advanced flood severity detection using ensemble learning models." Journal of Physics: Conference Series 1916, no. 1 (2021): 012048. http://dx.doi.org/10.1088/1742-6596/1916/1/012048.

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Abuassba, Adnan O. M., Dezheng Zhang, Xiong Luo, Ahmad Shaheryar, and Hazrat Ali. "Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines." Computational Intelligence and Neuroscience 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/3405463.

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Extreme Learning Machine (ELM) is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN). It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemble (AELME) for classification, which includes Regularized-ELM, L2-norm-optimized ELM (ELML2), and Kernel-ELM. The ensemble is constructed by training a randomly chosen ELM classifier on a subset of train
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B M, Rakshitha. "Ensemble Learning Frameworks in Cardiovascular Prognostics: Advancements in Predictive Analytics." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 2048–58. https://doi.org/10.22214/ijraset.2025.72558.

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Cardiovasculardisease remains a pervasive and serious global health concern, underscoring the necessity of accurate and timely risk assessment. Within the field of machine learning, ensemble methods have gained significant traction for their ability to predict cardiovascular outcomes. Established algorithms—such as Support Vector Machines, Random Forests, and Gradient Boosting—continue to serve as reliable mainstays. Recently, however, advanced ensemble approaches like stacking and CatBoost have garnered increased attention. Emerging research suggests these newer methodologies may, in some ins
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Alserhani, Faeiz, and Alaa Aljared. "Evaluating Ensemble Learning Mechanisms for Predicting Advanced Cyber Attacks." Applied Sciences 13, no. 24 (2023): 13310. http://dx.doi.org/10.3390/app132413310.

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With the increased sophistication of cyber-attacks, there is a greater demand for effective network intrusion detection systems (NIDS) to protect against various threats. Traditional NIDS are incapable of detecting modern and sophisticated attacks due to the fact that they rely on pattern-matching models or simple activity analysis. Moreover, Intelligent NIDS based on Machine Learning (ML) models are still in the early stages and often exhibit low accuracy and high false positives, making them ineffective in detecting emerging cyber-attacks. On the other hand, improved detection and prediction
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Krishnamoorthy, Latha, and Ammasandra Sadashivaiah Raju. "An ensemble approach for electrocardiogram and lip features based biometric authentication by using grey wolf optimization." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 1524. http://dx.doi.org/10.11591/ijeecs.v33.i3.pp1524-1535.

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In the pursuit of fortified security measures, the convergence of multimodal biometric authentication and ensemble learning techniques have emerged as a pivotal domain of research. This study explores the integration of multimodal biometric authentication and ensemble learning techniques to enhance security. Focusing on lip movement and electrocardiogram (ECG) data, the research combines their distinct characteristics for advanced authentication. Ensemble learning merges diverse models, achieving increased accuracy and resilience in multimodal fusion. Harmonizing lip and ECG modalities establi
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Krishnamoorthy, Latha, and Ammasandra Sadashivaiah Raju. "An ensemble approach for electrocardiogram and lip features based biometric authentication by using grey wolf optimization." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 1524–35. https://doi.org/10.11591/ijeecs.v33.i3.pp1524-1535.

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In the pursuit of fortified security measures, the convergence of multimodal biometric authentication and ensemble learning techniques have emerged as a pivotal domain of research. This study explores the integration of multimodal biometric authentication and ensemble learning techniques to enhance security. Focusing on lip movement and electrocardiogram (ECG) data, the research combines their distinct characteristics for advanced authentication. Ensemble learning merges diverse models, achieving increased accuracy and resilience in multimodal fusion. Harmonizing lip and ECG modalities establi
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Darji, Pinesh Arvindbhai. "Utilizing an Ensemble of Extra Tree Model for Classifying Mesothelioma Cancer." African Journal of Biological Sciences 6, no. 12 (2024): 535–45. http://dx.doi.org/10.48047/afjbs.6.12.2024.535-545.

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Objectives: Explore the potential of ensemble learning techniques like Bagging Tree, Random Forest, and Ensemble Extra Tree in transforming mesothelioma diagnosis.Overcome challenges associated with late-stage detection and limited treatment options using advanced machine learning algorithms.Enhance predictive power and feature extraction capabilities through the combination of diverse ensemble algorithms.
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Nyaramneni, Sarika, Tejaswi Potluri, and Jahnavi Somavarapu. "Advanced Ensemble Machine Learning Models to Predict SDN Traffic." Procedia Computer Science 230 (2023): 417–26. http://dx.doi.org/10.1016/j.procs.2023.12.097.

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Dissertations / Theses on the topic "Advanced Ensemble Learning"

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Luong, Vu A. "Advanced techniques for classification of non-stationary streaming data and applications." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/420554.

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Today we are going through the Industry 4.0, where not only people are connected via social networks, but an enormous number of electronic devices are also connected via the Internet of Things (IoT). With the rapid development of modern technologies like blockchains, 5G, computing chips, software infrastructures, people and application programs are interacting with each other at a very fast pace. As a result, massive amount of data is generated in real time, posing many interesting but challenging problems to the machine learning community. According to the International Data Corporation [73],
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Wang, Xian Bo. "A novel fault detection and diagnosis framework for rotating machinery using advanced signal processing techniques and ensemble extreme learning machines." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3951596.

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Reichenbach, Jonas. "Credit scoring with advanced analytics: applying machine learning methods for credit risk assessment at the Frankfurter sparkasse." Master's thesis, 2018. http://hdl.handle.net/10362/49557.

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Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies Management<br>The need for controlling and managing credit risk obliges financial institutions to constantly reconsider their credit scoring methods. In the recent years, machine learning has shown improvement over the common traditional methods for the application of credit scoring. Even small improvements in prediction quality are of great interest for the financial institutions. In this thesis classification methods are appli
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King, SM. "The conductor-teacher, conductor-learner : an autoethnography of the dynamic conducting/teaching, learning process of an advanced level wind ensemble conductor." Thesis, 2011. https://eprints.utas.edu.au/11714/1/Whole-Stephen_King_-_Masters_Thesis.pdf.

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This study aimed to examine the nature of the work of a conductor-music educator, more specifically my lived experience as a music educator, conductor and performer as I worked with a community music program in regional Tasmania, Australia. The study was conceived from a desire to better understand my own practice as a music educator and conductor. It is through this desire that I examine the nature of the conductor-music educator‘s work through my eyes and the eyes of members of an ensemble I conduct. A number of research studies have examined music educators‘ work and the conducting practice
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Books on the topic "Advanced Ensemble Learning"

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Paneerselvam, Surekha. Advanced Decision Sciences Based on Deep Learning and Ensemble Learning Algorithms: A Practical Approach Using Python. Nova Science Publishers, Incorporated, 2021.

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Paneerselvam, Surekha. Advanced Decision Sciences Based on Deep Learning and Ensemble Learning Algorithms: A Practical Approach Using Python. Nova Science Publishers, Incorporated, 2021.

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Coveney, Peter V., and Shunzhou Wan. Molecular Dynamics: Probability and Uncertainty. Oxford University PressOxford, 2025. https://doi.org/10.1093/9780198893479.001.0001.

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Abstract This book explores the intersection of molecular dynamics (MD) simulation with advanced probabilistic methodologies to address the inherent uncertainties in the approach. Beginning with a clear and comprehensible introduction to classical mechanics, the book transitions into the probabilistic formulation of MD, highlighting the importance of ergodic theory, kinetic theory, and unstable periodic orbits, concepts which are largely unknown to current practitioners within the domain. It discussed ensemble-based simulations, free energy calculations and the study of polymer nanocomposites
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Book chapters on the topic "Advanced Ensemble Learning"

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Marken, Gagandeep, and Sofiur Rahaman. "Boosting Accuracy: Advanced Ensemble Learning Strategies." In Advances in Intelligent Systems Research. Atlantis Press International BV, 2025. https://doi.org/10.2991/978-94-6463-716-8_24.

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Abbasian, Houman, Chris Drummond, Nathalie Japkowicz, and Stan Matwin. "Inner Ensembles: Using Ensemble Methods Inside the Learning Algorithm." In Advanced Information Systems Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40994-3_3.

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Liu, Feng, Fengzhan Tian, and Qiliang Zhu. "Bayesian Network Structure Ensemble Learning." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73871-8_42.

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Zhao, Qiang Li, Yan Huang Jiang, and Ming Xu. "Incremental Learning by Heterogeneous Bagging Ensemble." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17313-4_1.

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Wang, Guangtao, Xiaomei Yang, and Xiaoyan Zhu. "Single Classifier Selection for Ensemble Learning." In Advanced Data Mining and Applications. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49586-6_21.

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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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Bikku, Thulasi, K. P. N. V. Satyasree, T. Penchalaiah, and Jarugula Jyothi. "Breast Cancer Detection Using Ensemble Learning Model." In Advanced Technologies and Societal Change. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-99-2832-3_92.

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Pawar, Lokesh, Anuj Kumar Sharma, Dinesh Kumar, and Rohit Bajaj. "Advanced Ensemble Machine Learning Model for Balanced BioAssays." In Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing. CRC Press, 2020. http://dx.doi.org/10.1201/9780429354526-12.

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Min, Hu, Kaihan Wu, Minghao Tan, Junyan Lin, Yufan Zheng, and Choujun Zhan. "Ensemble Learning for Crowdfunding Dynamics: JingDong Crowdfunding Projects." In Neural Computing for Advanced Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6135-9_28.

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Ke, Qi, Rong Gao, Wun She Yap, Yee Kai Tee, Yan Chai Hum, and YuJian Gan. "Malaria Cell Images Classification with Deep Ensemble Learning." In Advanced Intelligent Computing in Bioinformatics. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5689-6_36.

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Conference papers on the topic "Advanced Ensemble Learning"

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Ankita and Sonam Mittal. "Predicting Stroke: Advanced Machine Learning Ensemble Techniques." In 2024 3rd International Conference for Advancement in Technology (ICONAT). IEEE, 2024. https://doi.org/10.1109/iconat61936.2024.10774844.

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Aggarwal, Dhruv, Vijay Mohan Shrimal, Raghav Meha, and Manoj Wadhwa. "Advanced Ensemble Learning Approaches Based Energy Consumption." In 2025 2nd International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE). IEEE, 2025. https://doi.org/10.1109/rmkmate64874.2025.11042729.

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Sang, Ningjing, Wenxiu Cai, Chang Yu, Mujie Sui, and Hao Gong. "Enhanced Investment Prediction via Advanced Deep Learning Ensemble." In 2024 IEEE 7th International Conference on Information Systems and Computer Aided Education (ICISCAE). IEEE, 2024. https://doi.org/10.1109/iciscae62304.2024.10761429.

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Khuat van, Duc, and Minh Tuan Nguyen. "Advanced Ensemble Machine Learning for ECG Arrhythmia Classification." In 2024 International Conference on Advanced Technologies for Communications (ATC). IEEE, 2024. https://doi.org/10.1109/atc63255.2024.10908266.

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Kanthavel, R., Adline Freeda R, R. Dhaya, Anju A, and Samantha Julianne S. "Advanced Sentiment Analysis Using Hybrid-Ensemble Deep Learning Techniques." In 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2025. https://doi.org/10.1109/iatmsi64286.2025.10985143.

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Prathyakshini, Prathwini, and Keerthana. "Optimization of Speech Emotion Recognition Through Advanced Ensemble Learning Techniques." In 2024 9th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2024. https://doi.org/10.1109/icces63552.2024.10859813.

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Nitasha, Aniket Kumar Pandey, Ankit Yadav, Subrat Mishra, and Medhanshi. "Enhancing Movie Recommendation Efficiency Using Ensemble Learning Techniques." In 2024 International Conference on Emerging Innovations and Advanced Computing (INNOCOMP). IEEE, 2024. http://dx.doi.org/10.1109/innocomp63224.2024.00094.

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Rahaman, Mohammed Ateequr, and Rasyidah Mohamad Idris. "Enhancing Energy Fraud Detection with Ensemble Learning Techniques." In 2024 IEEE International Conference on Advanced Power Engineering and Energy (APEE). IEEE, 2024. https://doi.org/10.1109/apee60256.2024.10790893.

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Gong, Yifei. "Ensemble Kernel-NN: Machine Learning with Hybrid Features." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11020361.

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Wang, chunyao, Jiaming Qian, and Chao Zuo. "Single-frame structured illumination microscopy based on ensemble learning." In Second Advanced Imaging and Information Processing Conference (AIIP 2024), edited by Xinzhu Sang. SPIE, 2024. http://dx.doi.org/10.1117/12.3046314.

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