A data-driven semi-supervised framework with adaptive quality filtering for imbalanced binary image classification

Imbalance-Aware Robust Representation Learning for Medical Image Binary Classification

Experiments show that IRRL achieves balanced classification performance, with favorable F1-score and Matthews Correlation Coefficient results that reflect improved minority-class recognition quality, and robustness and consistency of the proposed representation learning strategy.

Debiased label enhancement for class-imbalanced semi-supervised learning

A novel semi-supervised framework that disentangles pseudo-label generation from the classification task via designing a dedicated pseudo-label generator to align the class distributions between labeled and unlabeled data is proposed.

Adaptive Weighting–Synthetic Minority Oversampling Technique

Class imbalance is prevalent in real-world datasets. Minority samples are far fewer than majority samples. Traditional classifier design typically assumes balanced data, which causes classifiers to favor the majority class when faced with imbalanced datasets. Thus, there are high misclassification costs for minority classes in critical fields like healthcare and finance. Most existing oversampling methods for handling imbalance problems, such as SMOTE (the synthetic minority oversampling technique), suffer from limitations like noise sensitivity, failure to consider minority-class sub-cluster structures, and poor adaptability to the heterogeneity of sample distributions. This article addresses these issues by proposing a novel oversampling algorithm: the adaptive weighting–synthetic minority oversampling technique (AW-SMOTE). It consists of three progressive stages. In the first stage, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering identifies the distribution structure of the minority samples. It identifies potential sub-clusters while removing noise interference. A clear data foundation for subsequent sampling is established. In the second stage, the most representative boundary sample in each cluster is used to evaluate the weight of each cluster. The total number of synthetic samples is allocated to different clusters according to their weights. This provides global sample enhancement support. In the third stage, adaptive sample generation is performed within each cluster. It combines the two perspectives of boundary tightness and local density. The sigmoid function is used to dynamically adjust the weight ratio. Finally, new samples are synthesized in key regions to both preserve distribution characteristics and enhance discriminability in classification. Through experiments on standard datasets from the KEEL repository, the feasibility and effectiveness of this algorithm are demonstrated.

SVED-SMOTE: support vector enhanced density SMOTE oversampling technique for imbalanced data classification

Improved tri-training semi-supervised classification algorithm based on adaptive neighborhood entropy

Tri-training is a classic semi-supervised learning framework that improves classifier performance by exploiting unlabeled data. However, it suffers from invalid view redundancy assumption and severe pseudo-label noise in real-world applications, which leads to performance degradation. To address these problems, this paper proposes an improved Tri-training semi-supervised classification algorithm based on adaptive neighborhood entropy, denoted as ANET. The algorithm retains the basic Tri-training structure and introduces a lightweight noise suppression module. Specifically, it constructs local neighborhoods for pseudo-labeled samples through k-nearest neighbor search, quantifies sample reliability by calculating neighborhood distribution entropy, and removes low-confidence noise samples with an adaptive threshold. Experiments are conducted on nine UCI datasets with four evaluation metrics: accuracy, precision, recall, and F1-measure. Compared with the standard Tri-training and five state-of-the-art improved algorithms, ANET achieves higher accuracy on 8 datasets, and the average accuracy is increased by 4.2%–8.7%. The results show that the algorithm effectively suppresses pseudo-label noise and has stronger robustness and practicality in real classification tasks.

An Imbalance-Resilient Deep Classification Framework for Reliable Analytics in Real-World Data Streams

Data streams in the real world often display severe class imbalance, with a critical under-representation of minority-class instances, resulting in biased and unreliable classification results from conventional deep learning frameworks. We introduce a new Imbalance-Resilient Deep Classification Framework that is defined for robust and stable analytics deployed on dynamic non-stationary data streams. The proposed framework combines adaptive resampling strategies, cost-sensitive learning mechanisms, and deep neural architectures to address the adverse impact of imbalanced class distributions. To cope with this, we propose a new dynamic threshold calibration module which gradually updates decision boundaries according to the changing characteristics of data streams in order to ensure relatively high classification accuracy overtime. The framework also includes an ensemble-driven feature extraction pipeline, enabling to learn discriminative representation for minority classes without compromising majority-class accuracy. The experiments conducted on benchmark and real-world imbalanced streaming datasets show that the proposed approach outperforms existing state-of-the-art algorithms, both in G-Mean, AUC-ROC and F1-Score metrics. Our findings validate the convenience, scalability and broad applicability of our framework in important applications like fraud detection, medical diagnosis, and network intrusion where imbalance constantly arises as a prominent issue.