Ensemble Learning

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18 papers in the last four weeks, up 38% on the four weeks before. 0.2% of all new papers.

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Machine Learning Classification of Cryopathy Syndromes: A Comprehensive Comparative Study
Jun 18, 2026Nataliya Shakhovska, Valentyna Chopyak, Ivan Izonin +1Ensemble Learning Clinical Decision Support
Lviv Polytechnic National University, 79013, Lviv, Ukraine · Danylo Halytskyi Lviv National University, 79010 Lviv, Ukraine
Kernel of Partition Paths: A Unified Representation for Tree Ensembles
Jun 17, 2026Nicolas MahlerFeature Attribution Ensemble Learning
Datapred SAS 23 rue Mirabeau 94300 Vincennes, France · Datapred SA EPFL Innovation Park – Bâtiment A 1015 Lausanne, Switzerland
Reducing Learner Redundancy in Boosting via Residual Orthogonalization
Jun 16, 2026Ye Su, Jipeng Guo, Yong Liu +5Ensemble Learning Residual Learning
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China · College of Information Science and Technology, Beijing University of Chemical Technology,2026 Beijing 100029, China · Gaoling School of Artificial Intelligence, Renmin University of China, Beijing 100872, China +2
Classifying by Proxy: Explainable and Reproducible Ensemble of Proxy Tasks for Child Sexual Abuse Imagery Classification
Jun 14, 2026Clara Ernesto, Carlos Caetano, Sandra Avila +3Explainable Artificial Intelligence Ensemble Learning
Instituto de Ciências Matemáticas e de Computação (ICMC), Universidade Estadual de São Paulo (USP), Brazil · Instituto de Computação (IC), Universidade Estadual de Campinas (UNICAMP), Brazil · Departamento de Ciência da Computação, Universidade Federal de Minas Gerais (UFMG), Brazil +1
Odds Law: The Decomposition Algebra On How Intelligence Organizes Itself to Solve Difficult Problems Reliably
Jun 14, 2026Hidayet AksuAgent Reliability Ensemble Learning
Fusing Stylometric and Embedding Systems to Estimate Authorship Likelihood Ratios in Japanese
Jun 12, 2026Praju Ghatpande, Satoru Tsuge, Shunichi Ishihara +2Ensemble Learning Digital Forensics
Co-Evolved Spiking Neural Network Ensembles via Marginal Contribution Fitness
Jun 12, 2026Catherine Rodriquez, James GhawalyEvolutionary Optimization Ensemble Learning
Louisiana State University Baton Rouge, Louisiana, USA
Majority-of-Three is Optimal
Jun 11, 2026Divit Rawal, Nikita ZhivotovskiyEnsemble Learning Classification
Department of Statistics, University of California, Berkeley.
Simplex-Constrained Sparse Bagging: Transitioning from Uniform Priors to Sparse Posteriors in Ensemble Learning
Jun 11, 2026Meher Sai Preetam Madiraju, Meher Bhaskar MadirajuModel Compression Probability Calibration
Validation-Stage Combinatorial Fusion Analysis for Imbalanced Credit-Card Fraud Detection
Jun 9, 2026Xiao Han, Chenyu WuClass-Imbalanced Learning Ensemble Learning
Goizueta Business School · Pratt School of Engineering
Improving PET/CT-Based Whole-Body Lesion Segmentation Using Prediction Uncertainty-Augmented Models
Jun 8, 2026Bashirul Azam Biswas, Biratal Raj Wagle, Zhihan Yang +4Ensemble Learning PET/CT Imaging
Department of Biomedical Data Science Geisel School of Medicine at Dartmouth Hanover, NH 03755, USA · Radiology Dartmouth Hitchcock Medical Center Lebanon, NH 03766, USA · Radiation Oncology Dartmouth Hitchcock Medical Center Lebanon, NH 03766, USA
Integrating Out, Twice:The Open-System Case That Neural-Network Ensemble Theory Is Missing
Jun 8, 2026Jin LeiEnsemble Learning Neural Network Training Dynamics
Department of Physics, Tongji University, Shanghai 200092, China · Southern Center for Nuclear-Science Theory (SCNT), Institute of Modern Physics, Chinese Academy of Sciences, Huizhou 516000, Guangdong Province, China
SHIELD-IDS: Structurally Heterogeneous Ensemble with Integrated Layered Defense for Intrusion Detection Systems
Jun 5, 2026Maryam Zaman, Muhammad Khuram ShahzadEnsemble Learning Network Intrusion Detection
School of Electrical Engineering and Computing(SEECS) · 2National University of Sciences and Technology, Islamabad, Pakistan
Tree-Based Formalization of Multi-Agent Complementarity in Human-AI Interactions
Jun 3, 2026Andrea FerrarioEnsemble Learning Human-AI Collaboration
Institute of Biomedical Ethics and History of Medicine, University of Zurich, Zurich, Switzerland · SUPSI, Dalle Molle Institute for Artificial Intelligence (IDSIA), Lugano, Switzerland · ETH Zurich, Zurich, Switzerland
An Ensembled Latent Factor Model via Differential Evolution and Gradient Descent Optimization
Jun 3, 2026Rui Zhang, Jinhang Liu, Wenbo ZhangRepresentation Learning Latent Variable Models
Chongqing Academy of Economics Research Chongqing, China · College of Computer and Information Science, Southwest University Chongqing, China
DLLG: Dynamic Logit-Level Gating of LLM Experts
Jun 3, 2026Bingnan Li, Zhaoyang Zhang, Xiaoze Liu +6Ensemble Learning Language Model Ensembles
AWS Agentic AI, Seattle, US · University of California San Diego, La Jolla, US · Purdue University, West Lafayette, US
How Many Trees in a Random Forest? A Revisited Approach with Plateau Search and Optuna Integration
Jun 2, 2026Vadim Porvatov, Andrey Dukhovny, Andrey LangeEnsemble Learning Random Forest
Sberbank, Moscow 117997, Russia · Skolkovo Institute of Science and Technology (Skoltech), Moscow 121205, Russia · Federal Research Center ‘‘Computer Science and Control" of Russian Academy of Sciences (FRC CSC RAS), Moscow 119333, Russia
A Comparative Analysis of Machine Learning Algorithms for Multi-Task Prediction of the Parameters of the Pectin Hydrolysis--Extraction Process
May 30, 2026Mullosharaf K. Arabov, Shavkat Yo. Kholov, Zainiddin K. MuhiddinEnsemble Learning Multi-Task Learning
Institute of Computational Mathematics and Information Technologies, Kazan Federal University, Kazan, Russia · Tajik Technical University named after Academician M.S. Osimi, Dushanbe, Tajikistan · V.I. Nikitin Institute of Chemistry, National Academy of Sciences of Tajikistan, Dushanbe, Tajikistan
A novel ordinal multi-view aggregation scheme for oak defoliation
May 27, 2026Francisco Bérchez-Moreno, Ricardo Enrique Hernández-Lambraño, David Guijo-Rubio +4Ensemble Learning Multi-View Learning
Departamento de Ciencia de la Computación e Inteligencia Artificial, Universidad de Córdoba, Campus Universitario de Rabanales, Edificio Albert Einstein. Ctra. N-IV, Km. 396, Córdoba, 14071, Andalucía, Spain · Department of Forest Engineering, Laboratory of Dendrochronology, Silviculture and Global Change – DendrodatLab, Universidad de Córdoba, Campus Universitario de Rabanales. Ctra. N-IV, Km. 396, Córdoba, 14071, Andalucía, Spain · Departamento de Teoría de la Señal y Comunicaciones, Universidad de Alcala, Escuela Politécnica. Campus Universitario. Ctra. Madrid-Barcelona, Km. 33,600, Madrid, 28805, Madrid, Spain +1
PINE: Pruning Boosted Tree Ensembles with Conformal In-Distribution Prediction Equivalence
May 27, 2026Haruki Yajima, Yusuke MatsuiModel Compression Ensemble Learning
The University of Tokyo.
Customer Churn Prediction on Structured Data Using FT-Transformer and Stacking Ensembles
May 26, 2026Joyjit Roy, Samaresh Kumar Singh, Laxmi ShawTransformer Ensemble Learning
Independent Researcher, Austin, TX, USA · Independent Researcher, Leander, TX · Texas A & M University-Victoria, Victoria, TX
Self-Ensembling Vision-Language Models for Chart Data Extraction
May 26, 2026Thomas Berkane, Qianyi Wang, Maimuna S. MajumderData Visualization Ensemble Learning
Computational Health Informatics Program, Boston Children’s Hospital & Harvard Medical School
CART Random Forests as Sequential Allocation over Random Opportunity Sets: A Stochastic-Control Theory of Ensemble Risk
May 26, 2026Tianxing Mei, Yingying Fan, Mingming Leng +1Ensemble Learning Statistical Learning Theory
Faculty of Business, Lingnan University · Data Sciences and Operations Department, University of Southern California
Electricity Consumption Forecasting: An Approach Using Cooperative Ensemble Learning with SHapley Additive exPlanations
May 25, 2026Eduardo Luiz Alba, Gilson Adamczuk Oliveira, Matheus Henrique Dal Molin Ribeiro +1Multivariate Time Series Forecasting Long-Term Time Series Forecasting
Industrial & Systems Engineering Graduate Program (PPGEPS), Federal University of Technology-Parana (UTFPR), Via do Conhecimento, KM 0l—Fraron, Pato Branco 85503-390, PR, Brazil
Partial Fusion of Neural Networks: Efficient Tradeoffs Between Ensembles and Weight Aggregation
May 21, 2026Fabian Morelli, Stephan EcksteinInverse Optimal Transport Ensemble Learning
Department of Computer Science, University of Tübingen, Germany · Department of Mathematics, University of Tübingen, Germany.
FruitEnsemble: MLLM-Guided Arbitration for Heterogeneous ensemble in Fine-Grained Fruit Recognition
May 20, 2026Enhui Yu, Junhui Li, Ruitong Lu +2Agricultural Image Analysis Ensemble Learning
University of Science and Technology Liaoning · Chuzhou University · Yeshiva University
Decision-Path Patterns as Tree Reliability Signals: Path-based Adaptive Weighting for Random Forest Classification
May 20, 2026Youngjoon ParkEnsemble Learning Binary Classification
Independent Researcher
SAGE: Scalable Automatic Gating Ensemble for Confident Negative Harvesting in Fraud Detection
May 19, 2026Sudheer Tubati, Amit GoyalEnsemble Learning Hard Negative Mining
Amazon Music Seattle, USA · Amazon Music San Francisco, USA
A Family of Divergence Measures for Evaluating the Reconstruction Quality of Explainable Ensemble Trees
May 19, 2026Massimo Aria, Agostino Gnasso, Carmela IorioSurrogate Modeling Ensemble Learning
Department of Economics and Statistics, University of Naples2026 Federico II, Naples, Italy
Multi-Head Attention as Ensemble Nadaraya-Watson Estimation: Variance Reduction, Decorrelation, and Optimal Head Diversity
May 18, 2026Ernest FokouéSelf-Attention Ensemble Learning
School of Mathematics and Statistics, College of Science Rochester Institute of Technology Rochester, New York 14623, USA

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Machine Learning Classification of Cryopathy Syndromes: A Comprehensive Comparative Study

Nataliya Shakhovska, Valentyna Chopyak, Ivan Izonin, Vira Haievska

Lviv Polytechnic National University, 79013, Lviv, Ukraine · Danylo Halytskyi Lviv National University, 79010 Lviv, Ukraine

Ensemble Learning Clinical Decision Support Multiclass Classification

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Cryopathy syndromes are difficult to classify because laboratory patterns often overlap across diagnostic categories, while some diagnoses are rare. This makes routine interpretation of cryoglobulin-related tests challenging and increases dependence on expert judgment. The aim of this study was to develop and compare machine learning approaches for automated classification of cryopathy syndromes from laboratory data and to identify a practical strategy for clinical decision support. Methods: We analysed laboratory records from 2,686 patients assigned to 14 diagnostic categories. The dataset included demographic variables, cryoglobulin measurements, precipitation tests, and hemagglutinin and hemolysin titers. Data preprocessing included cleaning, encoding, imputation, normalization, and construction of clinically informed interaction features. We evaluated 12 modelling strategies, including Random Forest, Gradient Boosted Trees, Multi-Layer Perceptron, soft-voting ensembles, class balancing with Synthetic Minority Over-sampling Technique, hierarchical classification, period-aware models, targeted binary classifiers, and probability calibration. Performance was assessed using stratified train-test evaluation and stratified 5-fold cross-validation. The main metrics were macro-averaged F1 score, accuracy, Top-3 accuracy, and expected calibration error. The overall task proved difficult because of marked class imbalance and clinical overlap between diagnoses. The best multiclass performance was achieved by a soft-voting ensemble of Random Forest and Gradient Boosted Trees. Cross-validation confirmed stable performance for the balanced Random Forest model. Tree-based methods consistently outperformed the neural network model. Feature engineering improved discrimination, and the most informative predictors were derived cryoglobulin-based interaction features.

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