2026· International Journal of Machine Learning and Predictive Analytics· Vol 9, pp. 41-53· 0 citations
TL;DR
Comparative findings indicate that hybrid methods that combine adaptive resampling and cost sensitive loss functions are always better than simpler classifiers based on their F1-score, G-mean, and area under the precision-recall curve.
Abstract
The problem of class imbalance in machine learning classification is widely present and difficult across the machine learning area, especially in real-world tasks, including fraud detection, medical diagnosing, network intrusion detection and fault prediction. When this occurs, the minority population is more likely to capture the important occurrences and the conventional machine learning models normally focus on the majority population and give misleading accuracy with poor generalization and high costs of misclassification. This paper is the result of an extensive research into powerful machine learning techniques in the classification of imbalanced datasets. The paper presents a systematic review of theoretical underpinnings of learning imbalance, literature reviews on state-of-the-art methods, such as data, algorithm-level and ensemble based methods, and suggests a convergent system methodology to build a robust classifier. Linear resampling algorithms, cost-effective learning algorithms, hybrid ensemble algorithms, and imbalanced data evaluation metrics are discussed in details. An organized experimental procedure is described to measure robustness when imbalance ratios and various noise levels are changing. Comparative findings indicate that hybrid methods that combine adaptive resampling and cost sensitive loss functions are always better than simpler classifiers based on their F1-score, G-mean, and area under the precision-recall curve. The discussion demonstrates practical trade-offs between model performance, model complexity and interpretability. In the conclusion part, the paper highlights future research directions which include scalable imbalance learning, deep learning adaptations and domain aware evaluation strategies. The paper is an excellent source of information to a researcher and practitioner aiming at finding principled and effective solutions to imbalanced classification problems.
One of the major problems with machine learning is class imbalance, which occurs when the majority class is much larger than the minority one and the classifiers tend to learn the majority class rather than the minority ones, and hence make poor predictions in the latter scenario. This paper introduces an Intelligent Machine Learning Classifier System for Class Imbalance Learning and Improved Prediction Accuracy in a single framework that combines adaptive class balancing (ACCB), dynamic feature selection (DFS), hybrid ensemble learning (HEL) and confidence-based decision fusion (CFDF). Initially, data preprocessing is used to remove inconsistencies from the data and to normalize the distribution of features. To obtain a balanced learning, an Intelligent Adaptive Class Balancing Algorithm is used to generate representative minority samples and to reduce majority instances to be redundant. After that, a Dynamic Feature Importance Selection Algorithm determines the attributes that are discriminative and improve classification accuracy and decrease computational complexity. A Hybrid Ensemble Classification Algorithm using several machine learning classifiers is used to process the selected features to improve the robustness and generalization. Finally, a Confidence-Based Intelligent Prediction Algorithm is used to generate reliable prediction with the estimation of confidence. Experimental results on standard imbalanced datasets show that the accuracy, precision, recall, F1-score, G-Mean and ROC-AUC are always improved than the traditional machine learning methods. The proposed framework is able to effectively deal with the class imbalance, reduce the prediction bias, improve minority class detection and offer a scalable and interpretable solution for various real world intelligent decision support applications in different domains.
Prabha Ravi· Journal of Intelligent Decis...· 0 citations
Extreme class imbalance remains a persistent challenge in machine learning, particularly in high-impact domains such as fraud detection, medical diagnosis, and risk analysis, where minority classes represent critical outcomes. Conventional models often fail in such settings due to their bias toward majority classes, resulting in poor minority detection despite high overall accuracy. Although various data-level and algorithm-level techniques have been proposed, existing studies typically evaluate them in isolation and lack a comprehensive understanding of their effectiveness across different imbalance conditions. To address this gap, this study proposes a systematic comparative framework that integrates data-level resampling, cost-sensitive learning, and hybrid approaches to evaluate their performance under varying imbalance ratios and noise levels. Multiple benchmark datasets are utilized, and experiments are conducted using standardized preprocessing, controlled imbalance simulation, and repeated trials to ensure robustness. Performance is assessed using imbalance-aware metrics, including precision, recall, F1-score, and ROC-AUC. The results indicate that hybrid approaches consistently outperform standalone methods, achieving the most stable and balanced performance across all scenarios. In particular, hybrid models demonstrate superior minority class recall and F1-score while maintaining competitive precision, especially under extreme imbalance conditions. The primary goal of this research is to provide a comprehensive evaluation of imbalance-handling strategies and offer practical guidance for selecting appropriate techniques based on dataset characteristics. The findings highlight the importance of combining data-centric and model-centric approaches to enhance robustness and reliability in imbalanced learning environments. The results demonstrate up to a 32% improvement in recall compared to baseline models.
Tamsir Ariyadi, E. Noche, Nisha Pandey et al.· Journal of Data Science· 0 citations
Insurance claim fraud continues to pose substantial financial and operational challenges for insurance companies worldwide, particularly in auto insurance, where fraudulent cases form a small but highly impactful portion of overall claims. One of the significant technical difficulties in detecting such fraud is the severe class imbalance in real-world insurance datasets, where legitimate claims vastly outnumber fraudulent ones. This study presents a systematic performance evaluation of classical machine learning (ML) classification models for insurance fraud detection under conditions of extreme class imbalance. The proposed framework focuses on widely used supervised learning algorithms, including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF), with an emphasis on understanding their behavior when trained on imbalanced data. To mitigate class imbalance bias, the Synthetic Minority Oversampling Technique (SMOTE) is applied to the dataset before model training. Model performance is evaluated using multiple metrics such as accuracy, precision, recall, and F1-score, which provide a more reliable assessment than accuracy alone in fraud detection scenarios. Experimental results demonstrate that ensemble-based methods, particularly Random Forest, achieve superior performance in identifying minority class fraud cases while maintaining stable overall classification accuracy. This research provides practical insights into selecting suitable classical ML models for insurance fraud detection, supporting the development of reliable decision support systems for insurance providers operating with imbalanced data.
Garima Sharma· International Journal For Mu...· 0 citations
The skewedness of results when predicting diabetes is mostly due to uneven distribution of data, especially in reducing detection rates of real patients. These are errors which cause delay in treatment or incorrect diagnosis. This work suggests a counter plan to this assumption, which is Adaptive Synthetic Class Balancing with filtering by class ratio (ASCPF). This does not require siloing such methods as SMOTE or NearMiss, but it does change the way to create or make samples, as well as the way to achieve retention, depending on group size. We evaluate the performance of each strategy by considering rare cases, using Extra Trees Classifier. We ran tests on the following data collections: CDC Diabetes, Breast Cancer Wisconsin (Diagnostic), and Credit Card Fraud Detection, KDD Cup 1999 Intrusion Detection, PIMA, and BRFSS. In the performance trend, ASCPF demonstrated the highest accuracy, no matter compared to SMOTE or NearMiss. Take CDC Diabetes. Here, ASCPF got to 94.12% in accuracy, pulled 87.99% sensitivity, hit 94.10% ROC-AUC - way ahead of SMOTE’s 77.65% accuracy and NearMiss’s 90.47%. It was important to keep the original proportions of groups to avoid to create misbalanced distribution and without changing the results it helped to achieve adding or removing the frequencies for cases of very few occurrences. One distinctive feature is that it is a combination of creating smart fake samples and ratio control – a novel approach which makes ASCPF unique. The combination results in more uniform performance on classes with skewed distribution, especially in important health decisions.
Pankaj Beldar, Snehal M. Kamalapur, Priti Vaidya et al.· Sigma Journal of Engineering...· 0 citations
The study comes to the conclusion that headline accuracy is an unreliable guide in imbalanced medical prediction, that imbalance handling can change a model's practical usefulness, and that this benefit is strongly algorithm-dependent, meaning that the decision to resample should be based on the algorithm and the screening priorities rather than being applied consistently.
A. Oduroye, Temilade Opanuga, Esther Tosin Akanbi et al.· International journal of re...· 0 citations
Credit card fraud detection is a challenging task due to the increase in digital transactions, evolving fraud patterns, and severe class imbalance present in real-world datasets. Although several comparative studies have been done in this domain, many of them are limited to a single dataset and primarily focus on predictive performance. This study presents a comparative evaluation of Supervised and Unsupervised Machine Learning algorithms for credit card fraud detection across five diverse Kaggle datasets with varied sizes, class distributions, and feature representations, including PCA-transformed numerical datasets and real-world-like datasets having numerical and categorical features. Eleven Supervised Machine Learning algorithms, namely Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosting, Naïve Bayes, K-Nearest Neighbors, LightGBM, XGBoost, CatBoost, and AdaBoost, together with four Unsupervised Machine Learning algorithms, namely K-Means, DBSCAN, Isolation Forest, and One-Class SVM, were evaluated using a consistent experimental framework. To keep alignment with real-world transaction environments, no sampling, resampling, or class-balancing techniques were applied. Performance was assessed using Accuracy, Precision, Recall, F1-score, ROC AUC, PR AUC, MCC, learning time, and execution time. Experimental results show that supervised ensemble methods consistently outperformed unsupervised approaches across all datasets. XGBoost and CatBoost achieved consistently strong fraud detection performance in terms of ROC AUC, PR AUC, and MCC across diverse datasets while keeping reasonable computational efficiency. Their ability to achieve high fraud detection performance under severe class imbalance makes them suitable for practical fraud detection systems. The findings provide practical insights into the robustness, scalability, and suitability of machine learning algorithms for credit card fraud detection under varying real-world data conditions that helps researchers and scientist for its better understandings.
N. Mishra, Rakhi Garg· 2026 7th International Confe...· 0 citations
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