Synthetic oversampling methods for dealing with imbalanced classification problems have been widely studied. However, the current synthetic oversampling methods still cannot perform well when facing high-dimensional imbalanced financial data. The fai...
With the growing complexity of high-dimensional imbalanced datasets in critical fields such as medical diagnosis and bioinformatics, feature selection has become essential to reduce computational costs, alleviate model bias, and improve classificatio...
With the increasing growth of data dimensionality, feature selection has become a crucial step in a variety of machine learning and data mining applications. In fact, it allows identifying the most important attributes of the task at hand, improving...
The training machine learning algorithm from an imbalanced data set is an inherently challenging task. It becomes more demanding with limited samples but with a massive number of features (high dimensionality). The high dimensional and imbalanced dat...
Integrating machine learning (ML) with Statistical Process Control (SPC) is important for Industry 4.0 environments. Contemporary manufacturing data exhibit high-dimensionality, autocorrelation, non-stationarity, and class imbalance, which challenge...
The most common preprocessing techniques used to deal with datasets having high dimensionality and a low number of instances—or wide data—are feature reduction (FR), feature selection (FS), and resampling. This study explores the use of F...
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...
Class imbalance and high dimensionality are two major issues in several real-life applications, e.g., in the fields of bioinformatics, text mining and image classification. However, while both issues have been extensively studied in the machine learn...
Machine learning plays an important role in ligand-based virtual screening. However, conventional machine learning approaches tend to be inefficient when dealing with such problems where the data are imbalanced and features describing the chemical ch...
Multiclass classification in cancer diagnostics, using DNA or Gene Expression Signatures, but also classification of bacteria species fingerprints in MALDI-TOF mass spectrometry data, is challenging because of imbalanced data and the high number of d...
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...
The complexity of cancer development involves intricate interactions among multiple biomarkers, such as gene-environment interactions. Utilizing microarray gene expression profile data for cancer classification is anticipated to be effective, thus dr...
Online social networks, as platforms for personal expression, have evolved into complex networks integrating political and social dimensions. This evolution has shifted the focus of network governance from addressing hacking activities to mitigating...
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...
Imbalanced learning problems often occur in application scenarios and are additionally an important research direction in the field of machine learning. Traditional classifiers are substantially less effective for datasets with an imbalanced distribu...
Detecting pump-and-dump schemes involving cryptoassets with high-frequency data is challenging due to imbalanced datasets and the early occurrence of unusual trading volumes. To address these issues, we propose constructing synthetic balanced dataset...
This paper proposes a method called autoencoder with probabilistic LightGBM (AED-LGB) for detecting credit card frauds. This deep learning-based AED-LGB algorithm first extracts low-dimensional feature data from high-dimensional bank credit card feat...
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...
The quality of machine learning models can suffer when inappropriate data is used, which is especially prevalent in high-dimensional and imbalanced data sets. Data preparation and preprocessing can mitigate some problems and can thus result in better...
This paper proposes a method, called autoencoder with probabilistic random forest (AE-PRF), for detecting credit card frauds. The proposed AE-PRF method first utilizes the autoencoder to extract features of low-dimensionality from credit card transac...
In the industrial IoT, it is vital to detect anomalies in multivariate time series, yet it faces numerous challenges, including highly imbalanced datasets, complex and high-dimensional data, and large disparities across variables. Despite the recent...
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...
Knowledge discovery helps mitigate the shortcomings of classical machine learning, especially those so-called imbalanced, high-dimensional, and noisy data challenges. Adaptive combination of multiple models, voting and other data fusion strategies, a...
With the rapid increase in the number of cyber-attacks, detecting and preventing malicious behavior has become more important than ever before. In this study, we propose a method for detecting and classifying malicious behavior in host process data u...
Fault detection and remaining useful life (RUL) prediction are critical tasks in self-healing network (SHN) environments and industrial cyber–physical systems. These domains demand intelligent systems capable of handling dynamic, high-dimension...
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...
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...
The landscape of 5G communication introduces heightened risks from malicious attacks, posing significant threats to network security and availability. The unique characteristics of 5G networks, while enabling advanced communication, present challenge...
In the semiconductor manufacturing industry, achieving high yields constitutes one of the pivotal factors for sustaining market competitiveness. When confronting the substantial volume of high-dimensional, non-linear, and imbalanced data generated du...
Casting defects in turbine blades can significantly reduce an aero-engine’s service life and cause secondary damage to the blades when exposed to harsh environments. Therefore, casting defect detection plays a crucial role in enhancing aircraft...
Purpose: High-involvement experience products (HIEP) are generally characterized by a high value and difficult purchasing decision for customers, and a wrong decision will bring large losses to consumers, severely affecting their trust in enterprises...
Laser-induced breakdown spectroscopy (LIBS) is a rapid, cost-effective technique for elemental analysis that enables real-time measurements with minimal sample preparation. However, LIBS datasets are often high-dimensional and imbalanced, limiting th...
Sentiment analysis has played a primary role in text classification. It is an undoubted fact that some years ago, textual information was spreading in manageable rates; however, nowadays, such information has overcome even the most ambiguous expectat...
Hyperspectral imagery (HSI), as a core data carrier in remote sensing, plays a crucial role in many fields. Still, it also faces numerous challenges, including the curse of dimensionality, noise interference, and small samples. These problems severel...
24 May 2021
In this study, four models—logistic regression (LR), random forest (RF), linear support vector machine (SVM), and radial basis function (RBF)-SVM—were compared for their accuracy in determining mortality caused by road traffic injuries. They were tes...
Cyber-security systems collect information from multiple security sensors to detect network intrusions and their models. As attacks become more complex and security systems diversify, the data used by intrusion-detection systems becomes more dimensio...
In the face of ever-evolving cyber threats, modern intrusion detection systems (IDS) must achieve long-term adaptability without sacrificing performance on previously encountered attacks. Traditional IDS approaches often rely on static training assum...
Detecting financial fraud is a critical aspect of modern intelligent financial systems. Despite the advances brought by deep learning in predictive accuracy, challenges persist—particularly in capturing complex, high-dimensional nonlinear featu...
Rotor imbalance in quadrotor UAVs poses a critical challenge, compromising flight stability, increasing maintenance demands, and reducing overall operational efficiency. Traditional vibration analysis methods, such as Fast Fourier Transform (FFT) and...
Hyperspectral small target detection (HSTD) is a promising pixel-level detection task. However, due to the low contrast and imbalanced number between the target and the background spatially and the high dimensions spectrally, it is a challenging one....
Corporate bankruptcy prediction has become increasingly critical amid economic uncertainty. This study proposes a novel two-stage machine learning approach to enhance bankruptcy prediction accuracy, applied to Tokyo Stock Exchange-listed companies. F...
Predictive health management (PHM) plays a pivotal role in the maintenance of contemporary industrial systems, with the evaluation of the state of health (SOH) and the prediction of remaining useful life (RUL) constituting its central objectives. Nev...
To address the significant challenges in determining the single-well production of tight gas and shale gas after hydraulic fracturing, artificial intelligence (AI) methods were implemented. Machine learning (ML) algorithms such as random forest (RF),...
Insurance fraud detection is a significant challenge due to increasing fraudulent claims, class imbalance, and the increasing complexity of fraudulent behaviour. Traditional machine learning models often struggle to generalize effectively when applie...
In this paper, we propose the Adaptive Volcano Support Vector Machine (AVSVM)—a novel classification model inspired by the dynamic behavior of volcanic eruptions—for the purpose of enhancing malware detection. Unlike conventional SVMs tha...
In light of the escalating ubiquity of the Internet, the proliferation of cyber-attacks, coupled with their intricate and surreptitious nature, has significantly imperiled network security. Traditional machine learning methodologies inherently exhibi...
The rise in cyber security issues has caused significant harm to tech world and thus society in recent years. Intrusion detection systems (IDSs) are crucial for the detection and the mitigation of the increasing risk of cyber attacks. False and disre...
Early detection of drought stress in greenhouse tomato (Solanum lycopersicum) is an important issue. Real-time and nondestructive assessment of plant water status is possible by spectroscopy. However, spectral data often suffer from the problems of c...
Against the backdrop of global climate change and increasing ecological pressure, the refined monitoring of forest resources and accurate tree species identification have become essential tasks for sustainable forest management. Hyperspectral remote...
Cloud computing environments generate high-dimensional, large-scale, and highly dynamic network traffic, making intrusion diagnosis challenging due to evolving attack patterns, severe traffic imbalance, and limited availability of labeled data. To ad...
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