Class-Imbalanced Learning

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11 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

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RBMatch: Dual-Level Class Rebalancing for Semi-Supervised Building Footprint Extraction
Oct 6, 2026Akil Ahmad Taki, Shaikh Anowarul FattahClass-Imbalanced Learning Remote Sensing Image Segmentation
Department of EEE, Bangladesh University of Engineering and Technology(BUET) · Department of EEE, University of Asia Pacific
Hub for Outliers, Spokes for Inliers: Uniform Latent Space Construction for Dual-Mismatched Semi-Supervised Learning
Oct 6, 2026Li Yuan, Yaxin Hou, Jiawei Tang +2Class-Imbalanced Learning Semi-Supervised Learning
School of Computer Science and Engineering, Southeast University, Nanjing, China
A Data-Centric Review of Plant Disease Datasets: Taxonomy, Critical Analysis, Environmental Variability, and Implications for Precision Agriculture
Oct 5, 2026Aamir Hilal, Shabir Ahmad Sofi, Neeraj GoelClass-Imbalanced Learning Plant Disease Classification
Department of Information Technology, National Institute of Technology, Hazratbal Srinagar 190006, Jammu & Kashmir, India · Department of Computer Science and Engineering, Indian Institute of Technology, Ropar Rupnagar 140001, Punjab, India
On Impact of Loss Function on the Performance of Neural Networks in Melanoma Diagnosis
Oct 5, 2026Morgan May, Pierpaolo Dondio, Simon CatonClass-Imbalanced Learning Skin Lesion Classification
Technological University Dublin, Dublin, Ireland · Research Ireland Centre for Research Training in Machine Learning, ML-Labs, Dublin, Ireland · Dublin City University, Dublin, Ireland +1
Usefulness of Quantile-Aware Diffusion Modeling for Highly Imbalanced Tabular Data
Oct 5, 2026Abu Talha, Peng Liu, Souradyuti PaulClass-Imbalanced Learning Synthetic Data Augmentation
Indian Institute of Technology Bhilai, Durg, C.G. 491002, India · Loughborough University, Loughborough, LE11 3TU, United Kingdom
Fast Convergence through Distributed Augmentation for Class-Imbalanced Federated Learning
Oct 4, 2026Arathi Nair M, J. Harshan, Anwitaman DattaClass-Imbalanced Learning Data Augmentation
Amar Nath and Shashi Khosla School of Information Technology, Indian Institute of Technology Delhi, India · Department of Electrical Engineering, Indian Institute of Technology Delhi, India · College of Computing and Data Science, Nanyang Technological University, Singapore
CarveMix-RC: Addressing Rare-Class Imbalance Through Lesion-Aware Synthetic Augmentation for Brain Metastasis Segmentation
Sep 28, 2026Md Shibly Sadique, Md Fayaz Bin Hossen, Michael L. Evans +4Class-Imbalanced Learning Data Augmentation
Vision Lab, Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA · Interdisciplinary Schools, Old Dominion University, Norfolk, VA 23529, USA
The Entropy Triangle Method (ETM): A novel framework for the prevention of cardiac arrhythmia with a review of more than 10,000 patients
Sep 24, 2026Arman daliriClass-Imbalanced Learning Electrocardiography
Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
CRISP: Scalable Importance-Stratified Coresets for Imbalanced Tabular Learning
Sep 22, 2026Hardhik Mohanty, Indrayana Rustandi, Mohamadreza SheibaniClass-Imbalanced Learning Training Data Selection
University of Southern California Los Angeles, California, USA · Coinbase San Francisco, California, USA
Exposing Blind Spots in Deep Imbalanced Regression Evaluation
Sep 21, 2026Noah C. Puetz, Jens U. Brandt, Marc Hilbert +3Class-Imbalanced Learning ML Reproducibility
TH Köln · Leiden University · Toyota Racing
Density-Ratio Rescoring for Imbalanced Classification Using Raking Duals and Classifier Scores
Sep 20, 2026Dongha Kim, Seunghwan ParkClass-Imbalanced Learning Density Ratio Estimation
School of Mathematics, Statistics and Data Science, Sungshin Women’s University, 2, Bomun-ro 34da-gil, Seongbuk-gu, Seoul, 02844, Republic of Korea · Department of Information Statistics, Kangwon National University, 1, Kangwondaehak-gil, Chuncheon-si, Gangwon-do, 24341, Republic of Korea
Infectious Bovine Pinkeye Detection Using Computer Vision and Imbalance-Aware Learning
Sep 20, 2026Michael Abalo, Jameson Brennan, Hossein Moradi RekabdarkolaeeClass-Imbalanced Learning You Only Look Once
Business Analytics, Economics, and Information System Bowling Green State University, Bowling Green, Ohio · Animal Science Department South Dakota State University West River Research and Extension Center Rapid City, South Dakota
Bounded Adjustment with Reliability-Guided Embedding for Imbalanced Learning with Noisy Labels
Sep 14, 2026Mushir Akhtar, Akarsh J., M. Tanveer +1Class-Imbalanced Learning Long-Tail Learning
Department of Mathematics, Indian Institute of Technology Indore, India · Department of Electrical Engineering, Indian Institute of Technology Indore, India
FedLTLib: A Comprehensive Benchmark for Federated Long-Tail Learning
Sep 14, 2026Changkun Lin, Junxiao WangClass-Imbalanced Learning Non-IID Federated Learning
Guangzhou University, China
AUC Maximization from Biased Positive-unlabeled Data with Confidence
Sep 12, 2026Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi +3Class-Imbalanced Learning Positive-Unlabeled Learning
NTT, Inc.
The Accuracy Paradox: Empirical Diagnostic of Default Decision Thresholds in Multi-Label Enzyme Commission Prediction [With Code]
Sep 7, 2026Bilal Ahmad, Rajed MehmoodClass-Imbalanced Learning Multi-Label Classification
The Department of Electrical, Electronics and Telecommunication Engineering, UET Lahore, Pakistan · Department of Computer Science and Engineering, Jiangsu University, Jiangsu, China
Privacy, Robustness, and Fairness Trade-offs in Federated Intrusion Detection: Geometric Indistinguishability at the Aggregation Interface
Sep 3, 2026Adrita Rahman Tory, ABM Shawkat Ali, Md Abu Layek +1Class-Imbalanced Learning Federated Learning Aggregation
Bangladesh University of Business and Technology (BUBT), Mirpur-2, Dhaka-1216, Bangladesh · Jagannath University, Dhaka, Bangladesh · University of New South Wales (UNSW), ACT 2601, Australia
On the Reliability of Generative Augmentation: A Wasserstein-Based Theoretical and Empirical Study
Sep 1, 2026Chathurika S Abeykoon, Mathias Nthiani Muia, Mallory GoldsteinClass-Imbalanced Learning Synthetic Data Augmentation
Local Reference Geometry Residual Augmentation for Imbalanced Time Series Classification
Aug 31, 2026Chuanhang Qiu, Yanran Xu, Yue Wang +1Class-Imbalanced Learning Data Augmentation
School of Electronics and Computer Science University of Southampton Southampton, United Kingdom
Learning Dynamics of Logits Debiasing for Long-Tailed Semi-Supervised Learning
Aug 31, 2026Yue Cheng, Jiajun Zhang, Xiaohui Gao +2Class-Imbalanced Learning Semi-Supervised Learning
Beijing Jiaotong University · AntGroup · Northwestern Polytechnical University +1
AIA $^{2}$ : Attribute-Agnostic Imbalance Augmentation for Subgroup Robustness
Aug 31, 2026Hanshu Rao, Guangzeng Han, Xiaolei HuangClass-Imbalanced Learning Synthetic Data Augmentation
University of Memphis · University of Missouri
SMOTE-VAR: An Uncertainty-Aware Oversampling Method for Predicting Depression Remission in University Students
Aug 31, 2026Dang Nguyen, Arun Kumar A, Taylor A. Braund +8Class-Imbalanced Learning Mental Health
Applied Artificial Intelligence Initiative (A2I2), Deakin University, Geelong, VIC, Australia · Black Dog Institute, University of New South Wales, Sydney, NSW, Australia
TEAMMix: Taxonomy Enrichment Augmentation and Minority-augmented Mixing Strategy for LLM-enhanced Weak-Supervised Hierarchical Text Classification
Aug 11, 2026Jian Zhang, Zhuohao Yang, Songlin Lei +6Class-Imbalanced Learning Data Augmentation
School of Computer Science and Technology, Zhejiang University, Hangzhou, China · ZJU-UIUC Institute, Zhejiang University, Haining, China · Shaoxing K3i Technology Co. Ltd +1
Predicting Space Groups of Double Perovskites by LLM with Dynamic Few-Shot Learning
Aug 11, 2026Jongwon Park, Inhyo Lee, Junhyeong Lee +1Class-Imbalanced Learning Crystal Structure Prediction
Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea · KAIST InnoCORE PRISM-AI Center, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea · Department of AX, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
Unsupervised Domain Adaptation for Multitask Image Analysis in Realistic Context with Extreme Label Shift; Application to the CTAO first Large Sized Telescope
Aug 10, 2026Michaël Dell'aiera, Thomas Vuillaume, Alexandre BenoitClass-Imbalanced Learning Label Shift
CNRS LAPP, Annecy, 74940, France · Université Savoie Mont Blanc LISTIC, Annecy, 74940, France
FedTVD: Balancing Data Quality and Quantity for Robust Federated Learning
Aug 10, 2026Radwan Selo, Majid Kundroo, Taehong KimClass-Imbalanced Learning Non-IID Federated Learning
School of Information and Communication Engineering, Chungbuk National University, Cheongju, 28644, Republic of Korea
SoftMCC: An MCC-Brier Calibration Bridge for Threshold-Free Model Selection under Class Imbalance
Aug 10, 2026Özkan CanayClass-Imbalanced Learning Model Calibration
Department of Information Systems and Technologies Faculty of Computer and Information Sciences Sakarya University, Esentepe Campus 54050 Serdivan, Sakarya, Türkiye
Rectifying Geometric Misalignment: Online Source-Free Adaptation for Class-Imbalanced EEG
Aug 5, 2026Shiwen Chu, Shanglin Li, Motoaki Kawanabe +1Cross-Subject EEG Decoding Class-Imbalanced Learning
Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto, Japan · BIFOLD – Berlin Institute for the Foundations of Learning and Data, 10587 Berlin, Germany · Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara, Japan
Recurrent Contrastive Learning for Imbalanced Medical Image Classification
Aug 4, 2026Zhiyuan Zhu, Xinling Meng, Junxuan Yu +15Class-Imbalanced Learning Contrastive Learning
School of Biomedical Engineering, Medical School, Shenzhen University, China · Ultra-X AI Lab, Shenzhen University, Guangdong, China · School of Biomedical Engineering, South-Central Minzu University, Wuhan, China +4
Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data
Jul 31, 2026Pulock Das, Yina Hou, Md. Kamrozzaman Bhuiyan +1Class-Imbalanced Learning Clustering
Department of Computer Science Tennessee State University Nashville, TN, USA · Enosis Solutions Dhaka, Bangladesh · Department of Computer Science North Carolina A&T State University Greensboro, NC, USA

Oct 6, 2026, cs.CVJ/K move · Enter open · S save

RBMatch: Dual-Level Class Rebalancing for Semi-Supervised Building Footprint Extraction

Akil Ahmad Taki, Shaikh Anowarul Fattah

Department of EEE, Bangladesh University of Engineering and Technology(BUET) · Department of EEE, University of Asia Pacific

Class-Imbalanced Learning Remote Sensing Image Segmentation Building Footprint Extraction Self-Training Semantic Segmentation

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Accurate building footprint extraction from high-resolution remote sensing imagery is essential for urban planning, disaster response, and environmental monitoring. However, obtaining dense pixel-level annotations is costly, motivating the use of semi-supervised learning (SSL) to leverage unlabeled imagery. In remote sensing, severe foreground--background imbalance poses a particular challenge for self-training, as it can bias pseudo-label generation and the resulting unsupervised optimization toward the majority background class. We show that addressing this imbalance at only one stage is insufficient: balancing pseudo-label selection alone does not prevent background bias from re-emerging during unsupervised loss optimization, a failure mode we term \emph{imbalance leak}. To address this issue, we propose \textbf{RBMatch}, a dual-level class-rebalancing framework that jointly regulates pseudo-label generation and unsupervised optimization. RBMatch combines a supervised learning pathway with a self-training module comprising three components: adaptive class-specific thresholding (ACT) for balanced pseudo-label selection, confidence-aware class-balanced reweighting (CACBR) for mitigating class bias in the unsupervised loss, and distribution alignment (DAL) for matching the predicted unlabeled-data distribution to the labeled-data prior. Experiments on the WHU, INRIA, and Massachusetts building footprint datasets across labeled ratios of 1%--10% show that RBMatch consistently achieves the best building IoU and F1-score among the evaluated methods. The improvement is most pronounced on the highly imbalanced Massachusetts dataset, where RBMatch improves IoU by 1.37 points over the strongest baseline at a 1% labeling ratio and is the only method to outperform the fully supervised baseline across all twelve dataset--ratio settings.

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