imbalanced classification

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

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Libraries

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Datasets archive 2025-07-28

6 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

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Most implemented papers archive 2025-07-28

30 shown of 80 papers with code (211 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• MoleculeNet: A Benchmark for Molecular Machine Learning
  2 Mar 2017 5 repositories listed
  However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to…
• SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning
  26 Jan 2023 4 repositories listed Syntology ran 4 of 4 samples · 0 unverified
  The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization performance.
• Deep Reinforcement Learning for Imbalanced Classification
  5 Jan 2019 3 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  The agent finally finds an optimal classification policy in imbalanced data under the guidance of specific reward function and beneficial learning environment.
• AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets
  8 Apr 2024 2 repositories listed
  By dynamically selecting different anchors at each iteration it promotes class balance and prevents overfitting the initial decision boundary, thus promoting the discovery of new clusters of minority instances.
• The Effect of Balancing Methods on Model Behavior in Imbalanced Classification Problems
  30 Jun 2023 2 repositories listed
  To capture these changes, Explainable Artificial Intelligence tools are used to compare models trained on datasets before and after balancing.
• A Large-Scale Database for Graph Representation Learning
  16 Nov 2020 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  With the rapid emergence of graph representation learning, the construction of new large-scale datasets is necessary to distinguish model capabilities and accurately assess the strengths and weaknesses of each technique.
• MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler
  17 Oct 2020 2 repositories listed Syntology ran 14 of 18 samples · 4 unverified
  This makes MESA generally applicable to most of the existing learning models and the meta-sampler can be efficiently applied to new tasks.
• Long-tailed Recognition by Routing Diverse Distribution-Aware Experts
  5 Oct 2020 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  We take a dynamic view of the training data and provide a principled model bias and variance analysis as the training data fluctuates: Existing long-tail classifiers invariably increase the model variance and the…
• MNIST-MIX: A Multi-language Handwritten Digit Recognition Dataset
  8 Apr 2020 2 repositories listed
  In this letter, we contribute a multi-language handwritten digit recognition dataset named MNIST-MIX, which is the largest dataset of the same type in terms of both languages and data samples.
• M2m: Imbalanced Classification via Major-to-minor Translation
  1 Apr 2020 2 repositories listed
  In most real-world scenarios, labeled training datasets are highly class-imbalanced, where deep neural networks suffer from generalizing to a balanced testing criterion.
• Large-Scale Long-Tailed Recognition in an Open World
  10 Apr 2019 2 repositories listed
  We define Open Long-Tailed Recognition (OLTR) as learning from such naturally distributed data and optimizing the classification accuracy over a balanced test set which include head, tail, and open classes.
• LSH-DynED: A Dynamic Ensemble Framework with LSH-Based Undersampling for Evolving Multi-Class Imbalanced Classification
  24 Jun 2025 1 repository listed
  The classification of imbalanced data streams, which have unequal class distributions, is a key difficulty in machine learning, especially when dealing with multiple classes.
• A statistical theory of overfitting for imbalanced classification
  17 Feb 2025 1 repository listed
  Classification with imbalanced data is a common challenge in data analysis, where certain classes (minority classes) account for a small fraction of the training data compared with other classes (majority classes).
• A Mathematics Framework of Artificial Shifted Population Risk and Its Further Understanding Related to Consistency Regularization
  15 Feb 2025 1 repository listed
  Through this framework, we establish that the expected risk of the shifted population is the sum of the original population risk and a gap term, which can be interpreted as a consistency regularization term.
• Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap Class
  20 Dec 2024 1 repository listed
  %In this paper, we start with the observation that imbalanced data training of generative models trained imbalanced dataset which under-represent the minority class.
• GAT-RWOS: Graph Attention-Guided Random Walk Oversampling for Imbalanced Data Classification
  20 Dec 2024 1 repository listed
  Class imbalance poses a significant challenge in machine learning (ML), often leading to biased models favouring the majority class.
• Dist Loss: Enhancing Regression in Few-Shot Region through Distribution Distance Constraint
  20 Nov 2024 1 repository listed
  The results demonstrate that Dist Loss effectively mitigates the negative impact of imbalanced data distribution on model performance, achieving state-of-the-art results in sparse data regions.
• Zipfian Whitening
  1 Nov 2024 1 repository listed Syntology ran 1 of 4 samples · 3 unverified · 4 pointer-only (licence)
  The word embedding space in neural models is skewed, and correcting this can improve task performance.
• LiwTERM: A Lightweight Transformer-Based Model for Dermatological Multimodal Lesion Detection
  18 Oct 2024 1 repository listed
  Thus, this project aims to contribute to developing multimodal machine learning-based models to cope with the skin lesion classification task employing a lightweight transformer model.
• ECGN: A Cluster-Aware Approach to Graph Neural Networks for Imbalanced Classification
  15 Oct 2024 1 repository listed Syntology ran 9 of 11 samples · 2 unverified · 11 pointer-only (licence)
  We propose the Enhanced Cluster-aware Graph Network (ECGN), a novel method that addresses these issues by integrating cluster-specific training with synthetic node generation.
• Lightweight Transducer Based on Frame-Level Criterion
  5 Sep 2024 1 repository listed
  The transducer model trained based on sequence-level criterion requires a lot of memory due to the generation of the large probability matrix.
• Balancing the Scales: Reinforcement Learning for Fair Classification
  15 Jul 2024 1 repository listed
  Fairness in classification tasks has traditionally focused on bias removal from neural representations, but recent trends favor algorithmic methods that embed fairness into the training process.
• Convex space learning for tabular synthetic data generation
  13 Jul 2024 1 repository listed
  In this paper, we introduce a deep learning architecture (NextConvGeN) with a generator and discriminator component that can generate synthetic samples by learning to model the convex space of tabular data.
• Adaptive Parametric Activation
  11 Jul 2024 1 repository listed
  In this work, we delve deeper in this phenomenon by performing a comprehensive statistical analysis in the classification and intermediate layers of both balanced and imbalanced networks and we empirically show that…
• Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibration
  10 Jun 2024 1 repository listed Syntology ran 11 of 24 samples · 13 unverified
  In contrast to the standard class-conditional CP (CCP) method that uniformly thresholds the class-wise conformity score for each class, the augmented label rank calibration step allows RC3P to selectively iterate this…
• Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance
  5 Jun 2024 1 repository listed
  In the context of imbalanced data, LLMs are used to oversample underrepresented groups and have shown promising improvements.
• Selective Mixup Fine-Tuning for Optimizing Non-Decomposable Objectives
  27 Mar 2024 1 repository listed Syntology ran 4 of 5 samples · 1 unverified · 5 pointer-only (licence)
  We find that current state-of-the-art empirical techniques offer sub-optimal performance on these practical, non-decomposable performance objectives.
• An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced Classification
  22 Mar 2024 1 repository listed
  In particular, many candidate models may exhibit very similar predictive performance, called the Rashomon effect, in model selection, and they may even produce different predictions for the same observations.
• Mixture of Mixups for Multi-label Classification of Rare Anuran Sounds
  14 Mar 2024 1 repository listed
  Multi-label imbalanced classification poses a significant challenge in machine learning, particularly evident in bioacoustics where animal sounds often co-occur, and certain sounds are much less frequent than others.
• iBRF: Improved Balanced Random Forest Classifier
  14 Mar 2024 1 repository listed
  Our proposed hybrid sampling technique, when incorporated into the framework of the Random Forest classifier, termed as iBRF: improved Balanced Random Forest classifier, achieves better prediction performance than other…

Syntology lines on 9 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.