The class imbalance problem has been a hot topic in the machine learning community in recent years. Nowadays, in the time of big data and deep learning, this problem remains in force. Much work has been performed to deal to the class imbalance proble...
This survey explores recent advances in addressing class imbalance issues for developing machine learning models in precision agriculture, with a focus on techniques used for plant disease detection, soil management, and crop classification. We exami...
Recent technological developments have led to an increase in the size and types of data in the medical field derived from multiple platforms such as proteomic, genomic, imaging, and clinical data. Many machine learning models have been developed to s...
Sensing the nighttime economy–housing imbalance is of great importance for urban planning and commerce. As an efficient tool of social sensing and human observation, mobile phone data provides an effective way to address this issue. In this pap...
Bone age assessment (BAA) is an important indicator of child maturity. Generally, a person is evaluated for bone age mostly during puberty stage; compared to toddlers and post-puberty stages, the data of bone age at puberty stage are much easier to o...
An innovative strategy for organizations to obtain value from their large datasets, allowing them to guide future strategic actions and improve their initiatives, is the use of machine learning algorithms. This has led to a growing and rapid applicat...
Due to the distributed data collection and learning in federated learnings, many clients conduct local training with non-independent and identically distributed (non-IID) datasets. Accordingly, the training from these datasets results in severe perfo...
We deal with the problem of class imbalance in data mining and machine learning classification algorithms. This is the case where some of the class labels are represented by a small number of examples in the training dataset compared to the rest of t...
With the escalating threat posed by network intrusions, the development of efficient intrusion detection systems (IDSs) has become imperative. This study focuses on improving detection performance in programmable logic controller (PLC) network securi...
Considering the sensitivity of data in medical scenarios, federated learning (FL) is suitable for applications that require data privacy. Medical personnel can use the FL framework for machine learning to assist in analyzing large-scale data that are...
In the rapid development of the Internet of Things (IoT) and large-scale distributed networks, Intrusion Detection Systems (IDS) face significant challenges in handling complex spatiotemporal features and addressing data imbalance issues. This articl...
The safety and reliability of high-speed train electric traction systems are crucial. However, the operating environment for China Railway High-speed (CRH) trains is challenging, with severe working conditions. Dataset imbalance further complicates f...
This study addresses the challenges of poorly annotated data and class imbalance in mental health detection from social media. We propose an integrated approach combining weak classifiers with gradient boosting, leveraging LSTM, pretrained Transforme...
Due to its high sensitivity, electrohysterography (EHG) has emerged as an alternative technique for predicting preterm labor. The main obstacle in designing preterm labor prediction models is the inherent preterm/term imbalance ratio, which can give...
Battery health monitoring and remaining useful life (RUL) estimation for electric vehicles face two significant challenges: battery data heterogeneity and sample imbalance. This study presents a novel approach based on Transformer architecture to spe...
Crash data analysis is key to improving road safety, but imbalanced data challenges accurate predictions for severe crashes, often leading to biased outcomes. This study investigates crash severity among young drivers (aged 17–24) in England, u...
An open problem impeding the use of deep learning (DL) models for forecasting land cover (LC) changes is their bias toward persistent cells. By providing sample weights for model training, LC changes can be allocated greater influence in adjustments...
Models that can predict battery cells’ thermal and electrical behaviors are necessary for real-time battery management systems to regulate the imbalance within battery cells. This work introduces a Gaussian Process Regression (GPR)-based data-driven...
Hit-and-run (HR) crashes refer to crashes involving drivers of the offending vehicle fleeing incident scenes without aiding the possible victims or informing authorities for emergency medical services. This paper aims at identifying significant predi...
Safe and stable operation of the aircraft hydraulic system is of great significance to the flight safety of an aircraft. Any fault may be a threat to flight safety and may lead to enormous economic losses and even human casualties. Hence, the normal...
Nowadays, breast cancer is the most frequent cancer among women. Early detection is a critical issue that can be effectively achieved by machine learning (ML) techniques. Thus in this article, the methods to improve the accuracy of ML classification...
Machine tools are used in a wide range of applications, and they can manufacture workpieces flexibly. Furthermore, they require maintenance; the overall costs include maintenance costs, which constitute a significant portion, and the costs involved i...
The approach of federated learning (FL) addresses significant challenges, including access rights, privacy, security, and the availability of diverse data. However, edge devices produce and collect data in a non-independent and identically distribute...
Disengagement of students during online learning significantly impacts the effectiveness of online education. Thus, accurately estimating when students are not engaged is a critical aspect of online-learning research. However, the inherent characteri...
Imbalanced data present a pervasive challenge in many real-world applications of statistical and machine learning, where the instances of one class significantly outnumber those of the other. This paper examines the impact of class imbalance on the p...
Synthetic aperture radar (SAR) ship recognition can obtain location and class information from SAR scene images, which is important in military and civilian fields, and has turned into a very important research focus recently. Limited by data conditi...
Plasma-based semiconductor processing is highly sensitive, thus even minor changes in the procedure can have serious consequences. The monitoring and classification of these equipment anomalies can be performed using fault detection and classificatio...
Data imbalance is a serious problem in machine learning that can be alleviated at the data level by balancing the class distribution with sampling. In the last decade, several sampling methods have been published to address the shortcomings of the in...
The rapidly increasing installed capacity of Wind Turbines (WTs) worldwide emphasizes the need for Operation and Maintenance (O&M) strategies favoring high availability, reliability, and cost-effective operation. Optimal decision-making and plann...
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Identifying high-risk drivers before an accident happens is necessary for traffic accident control and prevention. Due to the class-imbalance nature of driving data, high-risk samples as the minority class are usually ill-treated by standard classifi...
Data4HumanRights’ training materials have been developed as open-source and tailored to limited-resource settings, where community data collectors often live and work. Access to training on data collection, analysis, and visualisation to suppor...
Paper recommendation systems are important for alleviating academic information overload. Such systems provide personalized recommendations based on implicit feedback from users, supplemented by their subject information, citation networks, etc. Howe...
The analysis in this study covers how power imbalance, alliance cohesion, diplomatic and media framing, and big data analytics affect scaling up in the conflict in a multipolar world. This research applies the Constructivist International Relations T...
Terrorism poses a significant threat to international peace and stability. The ability to predict potential casualties resulting from terrorist attacks, based on specific attack characteristics, is vital for protecting the safety of innocent civilian...
Continuously acquired biosignals from patient monitors contain significant amounts of unusable data. During the development of a decision support system based on continuously acquired biosignals, we developed machine and deep learning algorithms to a...
The recent introduction of smart manufacturing, also called the ‘smart factory’, has made it possible to collect a significant number of multi-variate data from Internet of Things devices or sensors. Quality control using these data in th...
Current age estimation datasets often have a skewed long-tail distribution with significant data imbalance, rather than an ideal uniform distribution for each category. The existing age estimation algorithms that rely on label distribution do not lev...
Electrocardiography (ECG) plays a pivotal role in monitoring cardiac health, yet the manual analysis of ECG signals is challenging due to the complex task of identifying and categorizing various waveforms and morphologies within the data. Additionall...
Lifelogs are generated in our daily lives and contain useful information for health monitoring. Nowadays, one can easily obtain various lifelogs from a wearable device such as a smartwatch. These lifelogs could include noise and outliers. In general,...
There are two useful methods of current measurement based on synthetic aperture radar (SAR): one is along-track interferometry (ATI), and the other is Doppler centroid analysis (DCA). For the ATI method, the interferometric phase must be accurate eno...
Industrial control systems (ICSs) are increasingly interconnected with enterprise IT networks and remote services, which expands the attack surface of operational technology (OT) environments. However, collecting sufficient attack traffic from real O...
Power quality studies for distribution networks are very important for future network expansions realized by utility companies, so the accuracy of such studies is critical. Load data, including information on load imbalance, could have in many situat...
Reliably detecting COVID-19 is critical for diagnosis and disease control. However, imbalanced data in medical datasets pose significant challenges for machine learning models, leading to bias and poor generalization. The dataset obtained from the EP...
Classification problems due to data imbalance occur in many fields and have long been studied in the machine learning field. Many real-world datasets suffer from the issue of class imbalance, which occurs when the sizes of classes are not uniform; th...
In this study, we developed a novel data-driven fault detection and diagnosis (FDD) method for bearing faults in induction motors where the fault condition data are imbalanced. First, we propose a bearing fault detector based on convolutional neural...
Multiple studies presented satisfactory performances for the treatment of various ocular diseases. To date, there has been no study that describes a multiclass model, medically accurate, and trained on large diverse dataset. No study has addressed a...
More and more Android application developers are adopting many different methods against reverse engineering, such as adding a shell, resulting in certain features that cannot be obtained through decompilation, which causes a serious sample imbalance...
Semi-supervised learning (SSL) is a popular research area in machine learning which utilizes both labeled and unlabeled data. As an important method for the generation of artificial hard labels for unlabeled data, the pseudo-labeling method is introd...
Data imbalance is often encountered in deep learning process and is harmful to model training. The imbalance of hard and easy samples in training datasets often occurs in the segmentation tasks from Contrast Tomography (CT) scans. However, due to the...
Learning from multiclass imbalanced data streams with concept drift and variable class imbalance ratios under a limited label budget presents new challenges in the field of data mining. To address these challenges, this paper proposes an adaptive act...
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