Imbalanced data situations exist in most fields of endeavor. The problem has been identified as a major bottleneck in machine learning/data mining and is becoming a serious issue of concern in food processing applications. Inappropriate analysis of a...
The Air Quality Index (AQI) dataset contains information on measurements of pollutants and ambient air quality conditions at certain location that can be used to predict air quality. Unfortunately, this dataset often has many missing observations and...
Distracted driving leads to a significant number of road crashes worldwide. Smartphone use is one of the most common causes of cognitive distraction among drivers. Available data on drivers’ phone use presents an invaluable opportunity to ident...
The increasing complexity of social science data and phenomena necessitates using advanced analytical techniques to capture nonlinear relationships that traditional linear models often overlook. This chapter explores the application of machine learni...
Crash severity is undoubtedly a fundamental aspect of a crash event. Although machine learning algorithms for predicting crash severity have recently gained interest by the academic community, there is a significant trend towards neglecting the fact...
The developments in the fields of industrial Internet of Things (IIoT) and big data technologies have made it possible to collect a lot of meaningful industrial process and quality-based data. The gathered data are analyzed using contemporary statist...
Class imbalance is a phenomenon of asymmetry that degrades the performance of traditional classification algorithms such as the Support Vector Machine (SVM) and Extreme Learning Machine (ELM). Various modifications of SVM and ELM have been proposed t...
Women exhibit marked physiological transformations in pregnancy, mandating regular and holistic assessment. Maternal and fetal vitality is governed by a spectrum of clinical, demographic, and lifestyle factors throughout this critical period. The exi...
The accurate prediction of brain stroke is critical for effective diagnosis and management, yet the imbalanced nature of medical datasets often hampers the performance of conventional machine learning models. To address this challenge, we propose a n...
Object classification in hyperspectral images involves accurately categorizing objects based on their spectral characteristics. However, the high dimensionality of hyperspectral data and class imbalance pose significant challenges to object classific...
Human activity recognition has become essential to a wide range of applications, such as smart home monitoring, health-care, surveillance. However, it is challenging to deliver a sufficiently robust human activity recognition system from raw sensor d...
Test-time adaptation (TTA) enhances model performance in target domains by dynamically adjusting parameters using unlabeled test data. However, existing TTA methods typically assume balanced data distributions, whereas real-world test data is often i...
The proportion of instances belonging to each class in a data-set plays an important role in machine learning. However, the real world data often suffer from class imbalance. Dealing with multi-class tasks with different misclassification costs of cl...
As software systems become increasingly large, the logic becomes more complex, resulting in a large number of bug reports being submitted to the bug repository daily. Due to tight schedules and limited human resources, developers may not have enough...
Chronic heart failure (CHF) is a clinical syndrome characterised by symptoms and signs due to structural and/or functional abnormalities of the heart. CHF confers risk for cardiovascular deterioration events which cause recurrent hospitalisations and...
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...
Class imbalance is a prevalent problem that not only reduces the performance of the machine learning techniques but also causes the lacking of the inherent complex characteristics of data. Though the researchers have proposed various ways to deal wit...
Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models from achieving satisfactory results. ID is the occurrence of a situation where the quantity of the samples belonging to one class outnumbers that of the other by a wide margin...
Imbalanced data are ubiquitous in many real-world applications, and they have drawn a significant amount of attention in the field of data mining. A variety of methods have been proposed for imbalanced data classification, and data sampling methods a...
18 February 2021
Along with the rapid demographic change, there has been increased attention to the risk of vehicle crashes relative to older drivers. Due to senior involvement and their physical vulnerability, it is crucial to develop models that accurately predict...
Imbalanced class data are commonly observed in pattern analysis, machine learning, and various real-world applications. Conventional approaches often resort to resampling techniques in order to address the imbalance, which inevitably alter the origin...
Adaptive machine learning has increasing importance due to its ability to classify a data stream and handle the changes in the data distribution. Various resources, such as wearable sensors and medical devices, can generate a data stream with an imba...
The Industrial Internet of Things (IIoT) deals with vast amounts of data that must be safeguarded against tampering or theft. Identifying rare attacks and addressing data imbalances pose significant challenges in the detection of IIoT cyberattacks. I...
Imbalance classification is common in scenarios like fault diagnosis, intrusion detection, and medical diagnosis, where obtaining abnormal data is difficult. This article addresses a one-class problem, implementing and refining the One-Class Nearest-...
Background/Objectives: This study examines the effectiveness of different resampling methods and classifier models for handling imbalanced datasets, with a specific focus on critical healthcare applications such as cancer diagnosis and prognosis. Met...
Educational data mining is capable of producing useful data-driven applications (e.g., early warning systems in schools or the prediction of students’ academic achievement) based on predictive models. However, the class imbalance problem in edu...
The classification of imbalanced and overlapping data has provided customary insight over the last decade, as most real-world applications comprise multiple classes with an imbalanced distribution of samples. Samples from different classes overlap ne...
The investigation of travel mode choice is an essential task in transport planning and policymaking for predicting travel demands. Typically, mode choice datasets are imbalanced and learning from such datasets is challenging. This study deals with im...
Human activity recognition (HAR) is the study of the identification of specific human movement and action based on images, accelerometer data and inertia measurement unit (IMU) sensors. In the sensor based HAR application, most of the researchers use...
Wind power is cleaner and less expensive compared to other alternative sources, and it has therefore become one of the most important energy sources worldwide. However, challenges related to the operation and maintenance of wind farms significantly c...
In high-dimensional machine learning tasks, supervised feature extraction is essential for improving model performance, with Linear Discriminant Analysis (LDA) being a common approach. However, LDA tends to deliver suboptimal performance when dealing...
Acoustic sensing provides crucial data for anomalous sound detection (ASD) in condition monitoring. However, building a robust acoustic-sensing-based ASD system is challenging due to the unsupervised nature of training data, which only contain normal...
Support Vector Machine (SVM) is a popular kernel-based method for data classification that has demonstrated high efficiency across a wide range of practical applications. However, SVM suffers from several limitations, including the potential failure...
The presence of imbalance in data significantly complicates the classification task, including fuzzy systems. Due to a large number of instances of bigger classes, instances of smaller classes are not recognized correctly. Therefore, additional tools...
Deep learning is crucial in marine logistics and container crane error detection, diagnosis, and prediction. A novel deep learning technique using Long Short-Term Memory (LSTM) detected and anticipated errors in a system with imbalanced data. The LST...
The aviation industry generates vast amounts of data across multiple stakeholders, but critical faults and anomalies occur rarely, creating inherently imbalanced datasets that complicate machine learning applications. Traditional centralized approach...
This study presents an optimization for a distributed machine learning framework to achieve credit card fraud detection scalability. Due to the growth in fraudulent activities, this research implements the PySpark-based processing of large-scale tran...
The electrical losses in power systems are divided into non-technical losses (NTLs) and technical losses (TLs). NTL is more harmful than TL because it includes electricity theft, faulty meters and billing errors. It is one of the major concerns in th...
With the expansion of data scale and diversity, the issue of class imbalance has become increasingly salient. The current methods, including oversampling and under-sampling, exhibit limitations in handling complex data, leading to overfitting, loss o...
Geomagnetic field data have been found to contain earthquake (EQ) precursory signals; however, analyzing this high-resolution, imbalanced data presents challenges when implementing machine learning (ML). This study explored feasibility of principal c...
Credit evaluation has always been an important part of the financial field. The existing credit evaluation methods have difficulty in solving the problems of redundant data features and imbalanced samples. In response to the above issues, an ensemble...
The Sichuan Basin has abundant deep and ultra-deep natural gas resources, making it a primary target for exploration and the development of China’s oil and gas industry. However, during the drilling of ultra-deep wells in the Sichuan Basin, com...
Stroke prediction is a vital research area due to its significant implications for public health. This comparative study offers a detailed evaluation of algorithmic methodologies and outcomes from three recent prominent studies on stroke prediction....
Data-driven methods have prominently featured in the progressive research and development of modern condition monitoring systems for electrical machines. These methods have the advantage of simplicity when it comes to the implementation of effective...
Cyber security is identified as an emerging concern for information technology management in business and society, owing to swift advances in telecommunication and wireless technologies. Cyberspace security has had a tremendous impact on numerous cru...
Owning life insurance coverage that is not enough to pay for the expenses is called underinsurance, and it has been found to have a significant influence on the sustainability and financial health of families. However, insurance companies need to hav...
Early prediction of students’ learning performance and analysis of student behavior in a virtual learning environment (VLE) are crucial to minimize the high failure rate in online courses during the COVID-19 pandemic. Nevertheless, traditional...
Deep learning-related technologies have achieved remarkable success in the field of intelligent fault diagnosis. Nevertheless, the traditional intelligent diagnosis methods are often based on the premise of sufficient annotation signals and balanced...
Ovarian cancer stands out as one of the most formidable adversaries in women’s health, largely due to its typically subtle and nonspecific early symptoms, which pose significant challenges to early detection and diagnosis. Although existing dia...
Hamming space retrieval is a hot area of research in deep hashing because it is effective for large-scale image retrieval. Existing hashing algorithms have not fully used the absolute boundary to discriminate the data inside and outside the Hamming b...
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