Imbalanced learning occurs when rare but critical events are missed because classifiers are trained primarily on majority-class samples. This paper introduces WISEST, a locality-aware weighted-interpolation algorithm that generates synthetic minority...
The imbalanced classification problem is a key research in machine learning as the relevant algorithms tend to focus on the features and patterns of the majority class instead of insufficient learning of the minority class, resulting in unsatisfactor...
Natural rubber (NR), with its excellent mechanical properties, has been attracting considerable scientific and technological attention. Through molecular dynamics (MD) simulations, the effects of key structural factors on tensile stress at the molecu...
Although imbalanced data have been studied for many years, the problem of data imbalance is still a major problem in the development of machine learning and artificial intelligence. The development of deep learning and artificial intelligence has fur...
Road accidents caused by weather conditions in winter lead to higher mortality rates than in other seasons. The main causes of road accidents include human carelessness, vehicle defects, road conditions, and weather factors. If the risk of road accid...
For classification problems, an imbalanced dataset can seriously reduce the learning efficiency in machine learning. In order to solve this problem, many scholars have proposed a series of methods mainly from the data and algorithm levels. At the dat...
When some treatment labels are missing, the number of the observed treated group is much smaller than the control group. The imbalanced dataset can make treatment effect estimation more challenging. To address this issue, we develop an inference fram...
18 September 2024
Missing data and class imbalance hinder the accurate prediction of rare events such as dairy mastitis. Resampling and imputation are employed to handle these problems. These methods are often used arbitrarily, despite their profound impact on predict...
The inherent class imbalance and scarcity of labeled samples remain major challenges in hyperspectral image (HSI) classification. SMOTE-based methods alleviate class imbalance by generating synthetic minority-class samples. However, their interpolati...
When classifiers face imbalanced class distributions, they often misclassify minority class samples, consequently diminishing the predictive performance of machine learning models. Existing oversampling techniques predominantly rely on the selection...
Bank fraud detection faces critical challenges in imbalanced datasets, where fraudulent transactions are rare, severely impairing model generalization. This study proposes a Gaussian noise-based augmentation method to address class imbalance, contras...
Effective detection of electricity theft is essential to maintain power system reliability. With the development of smart grids, traditional electricity theft detection technologies have become ineffective to deal with the increasingly complex data o...
Conventional logging and elemental logging-based lithology identification for volcanic buried-hill reservoirs is hampered by overlapping logging responses, low vertical resolution of elemental measurements, and inter-sample class imbalance, which lea...
Class imbalance severely limits Network Intrusion Detection Systems (NIDSs) for minority Web attack classes: CICIDS2017 contains only 21 SQL Injection instances among 2.27 million benign flows. This study enriches CICIDS2017 with authentic SQL Inject...
Artificial intelligence applications in management, such as fraud detection and churn prediction, are frequently constrained by the class imbalance problem. Standard over-sampling methods, such as SMOTE, rely on local geometric interpolation, which a...
Landslide susceptibility mapping in alpine regions with limited data has two persistent obstacles: too few recorded landslide cases and the questionable reliability of non-landslide samples. We address these issues with a hybrid generative framework&...
Knee osteoarthritis (KOA) is a most prevalent chronic muscoloskeletal disorder causing pain and functional impairment. Accurate predictions of KOA evolution are important for early interventions and preventive treatment planning. In this paper, we pr...
Increasing road congestion is the main constraint that may influence the economic development of cities and urban freight transport efficiency because it generates additional costs related to delay, influences social life, increases environmental emi...