Accurate spatial distribution and area of crops are important basic data for assessing agricultural productivity and ensuring food security. Traditional classification methods tend to fit most categories, which will cause the classification accuracy...
Oversampling is common and effective in resolving the classification problem of imbalanced data. Traditional oversampling methods are prone to generating overlapping or noisy samples. Clustering can effectively alleviate the above problems to a certa...
Intrusion detection systems (IDSs) play a pivotal role in safeguarding networks and systems against malicious activities. However, the challenge of imbalanced datasets significantly impacts IDS research, skewing learning models towards the majority c...
Electricity theft has caused massive losses and damage to electricity utilities. The damage affects the electricity supply’s quality and increases the generation load. The losses happen not only for the electricity utilities but also affect the...
Software-defined networking (SDN) is becoming a predominant architecture for managing diverse networks. However, recent research has exhibited the susceptibility of SDN architectures to cyberattacks, which increases its security challenges. Many rese...
This study presents a novel feature-engineered–natural gradient descent ensemble-boosting (NGBoost) machine-learning framework for detecting fraud in power consumption data. The proposed framework was sequentially executed in three stages: data...
Rockburst is a common and huge hazard in underground engineering, and the scientific prediction of rockburst disasters can reduce the risks caused by rockburst. At present, developing an accurate and reliable rockburst risk prediction model remains a...
This study presents a novel localization framework that leverages the unique properties of chirp signals combined with a time division multiple access (TDMA)-based tactical data link to achieve high-precision positioning. Chirp signals, with their wi...
Dried Hami jujube has great commercial and nutritional value. Starch-head and mildewed fruit are defective jujubes that pose a threat to consumer health. A novel method for detecting starch-head and mildewed fruit in dried Hami jujubes with visible/n...
Accurately mapping crop distribution is important for environmental and food security applications. The success of machine learning algorithms (MLs) applied to mapping crops is partly dependent on the acquisition of sufficient training samples. Howev...
Background: Surgical site infections (SSIs) lead to higher hospital readmission rates and healthcare costs, representing a significant global healthcare burden. Machine learning (ML) has demonstrated potential in predicting SSIs; however, the challen...
The problems of imbalanced datasets are generally considered asymmetric issues. In asymmetric problems, artificial intelligence models may exhibit different biases or preferences when dealing with different classes. In the process of addressing class...
Cyanobacterial blooms appear by complex causes such as water quality, climate, and hydrological factors. This study aims to present the machine learning models to predict occurrences of these complicated cyanobacterial blooms efficiently and effectiv...
Machine learning (ML) has emerged as a powerful tool in transformer condition assessment, enabling more accurate diagnostics by leveraging historical test data. However, imbalanced datasets, often characterized by limited samples in poor transformer...
The authors wish to make the following correction to their paper [...]
This study addresses the challenge of multi-dimensional and small gas sensor data classification using a gelatin–carbon black (CB-GE) composite film sensor, achieving 91.7% accuracy in differentiating gas types (ethanol, acetone, and air). Key...
Predicting nurse turnover is a growing challenge within the healthcare sector, profoundly impacting healthcare quality and the nursing profession. This study employs the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance is...
This study was focused on deriving the MTSA-related accident reduction rate (ARR) required to calculate the safety benefits before and after expanding the scope of the system. By performing spatial analysis using geographic information system technol...
The increase in mining depth necessitates higher strength requirements for hard rock pillars, making mine pillar stability analysis crucial for pillar design and underground safety operations. To enhance the accuracy of predicting the stability state...
Achieving cyber-security has grown increasingly tricky because of the rising concern for internet connectivity and the significant growth in software-related applications. It also needs a robust defense system to defend itself from multiple cyberatta...
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...
Typically, classification is conducted on a dataset that consists of numerical features and target classes. For instance, a grayscale image, which is usually represented as a matrix of integers varying from 0 to 255, enables one to apply various clas...
The primary objective of this study is to examine the factors that contribute to the early prediction of Massive Open Online Courses (MOOCs) dropouts in order to identify and support at-risk students. We utilize MOOC data of specific duration, with a...
The Saarbruecken Voice Database (SVD) is a public database used by voice pathology detection systems. However, the distributions of the pathological and normal voice samples show a clear class imbalance. This study aims to develop a system for the cl...
An important problem associated with the aerial mapping of the seabed is the precise classification of point clouds characterizing the water surface, bottom, and bottom objects. This study aimed to improve the accuracy of classification by addressing...
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...
Cerebral strokes, the abrupt cessation of blood flow to the brain, lead to a cascade of events, resulting in cellular damage due to oxygen and nutrient deprivation. Contemporary lifestyle factors, including high glucose levels, heart disease, obesity...
In this paper, an experimental study was carried out to determine the influence of imbalanced datasets preprocessing in the performance of associative classifiers, in order to find the better computational solutions to the problem of credit scoring....
A physical model of a wireless transmission channel in the time domain usually consists of the main propagation path and only a few reflections. The reasonable assumptions made about the channel model can improve its parameters’ estimation by a...
23 February 2021
Hematopoietic cancer is a malignant transformation in immune system cells. Hematopoietic cancer is characterized by the cells that are expressed, so it is usually difficult to distinguish its heterogeneities in the hematopoiesis process. Traditional...
The automatic production of land use/land cover maps continues to be a challenging problem, with important impacts on the ability to promote sustainability and good resource management. The ability to build robust automatic classifiers and produce ac...
Techniques for estimating the plunger position have successfully proven to support operation and monitoring of electromagnetic actuators without the necessity of additional sensors. Sophisticated techniques in this field make use of an oversampled me...
Knowledge of animal behavior can be indicative of the well-being, health, productivity, and reproduction of animals. The use of accelerometers to classify and predict animal behavior can be a tool for continuous animal monitoring. Therefore, the aim...
Unbalanced data can have an impact on the machine learning (ML) algorithms that build predictive models. This manuscript studies the influence of oversampling and undersampling strategies on the learning of the Bayesian classification models that pre...
The number of sensing data are often imbalanced across data classes, for which oversampling on the minority class is an effective remedy. In this paper, an effective oversampling method called evolutionary Mahalanobis distance oversampling (EMDO) is...
Land cover maps are a critical tool to support informed policy development, planning, and resource management decisions. With significant upsides, the automatic production of Land Use/Land Cover maps has been a topic of interest for the remote sensin...
Breast cancer prediction datasets are usually class imbalanced, where the number of data samples in the malignant and benign patient classes are significantly different. Over-sampling techniques can be used to re-balance the datasets to construct mor...
With the wide application of wireless sensor networks in military and environmental monitoring, security issues have become increasingly prominent. Data exchanged over wireless sensor networks is vulnerable to malicious attacks due to the lack of phy...
In view of the data of fault diagnosis and good product testing in the industrial field, high-noise unbalanced data samples exist widely, and such samples are very difficult to analyze in the field of data analysis. The oversampling technique has pro...
One of the significant challenges in machine learning is the classification of imbalanced data. In many situations, standard classifiers cannot learn how to distinguish minority class examples from the others. Since many real problems are unbalanced,...
Class imbalance is prevalent in real-world datasets. Minority samples are far fewer than majority samples. Traditional classifier design typically assumes balanced data, which causes classifiers to favor the majority class when faced with imbalanced...
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...
Real-world datasets are heavily skewed where some classes are significantly outnumbered by the other classes. In these situations, machine learning algorithms fail to achieve substantial efficacy while predicting these underrepresented instances. To...
Intrusion analysis is essential for cybersecurity, but oftentimes, the overwhelming number of false alerts issued by security appliances can prove to be a considerable hurdle. Machine learning algorithms can automate a task known as security alert da...
The development of malign cells that can grow in any part of the stomach, known as gastric cancer, is one of the most common causes of death worldwide. In order to increase the survival rate in patients with this condition, it is essential to improve...
Background: Severe obesity is a global public health threat of growing proportions. Accurate models to predict severe postoperative complications could be of value in the preoperative assessment of potential candidates for bariatric surgery. So far,...
Cervical cancer affects a large portion of the female population, making the prediction of this disease using Machine Learning (ML) of utmost importance. ML algorithms can be integrated into complex, intelligent, agent-based systems that can offer de...
To alleviate the data imbalance problem caused by subjective and objective factors, scholars have developed different data-preprocessing algorithms, among which undersampling algorithms are widely used because of their fast and efficient performance....
The accuracy of coal mine water inrush prediction models is affected mainly by the small number of samples and difficulty in feature extraction. A new data augmentation water inrush prediction method is proposed. This method uses the natural neighbor...
The class imbalance problem has been reported to exist in remote sensing and hinders the classification performance of many machine learning algorithms. Several technologies, such as data sampling methods, feature selection-based methods, and ensembl...
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