Imbalanced data classification is a prevalent challenge in real-world applications. While a conventional sphere-based classification algorithm, random sphere cover (RSC), evenly constructs a set of spheres for two classes in balanced data using a parameter for…

This paper presents a novel end-to-end oversampling-classification approach, which we refer to as imbalanced data-classifying generative adversarial network (ImbGAN), for imbalanced data classification. ImbGAN has a classifier-embedded structure within a GAN a…

In semiconductor manufacturing processes, spatial defect patterns on semiconductor wafers can provide useful information to quality engineers regarding the root causes of abnormalities. As early detection of process problems increases the wafer yield, the auto…

Density peaks clustering (DPC), which is short for clustering by fast search-and-find of density peaks, is a recently developed density-based clustering method that is widely used because of its effective detection of isolated high-density regions. However, it…

Stringent global regulations aim to reduce nitrogen dioxide (NO2) emissions from maritime shipping. However, the lack of a global monitoring system makes compliance verification challenging. To address this issue, we propose a systematic approach to monitor sh…

The imbalance of classes in real-world datasets poses a major challenge in machine learning and classification, and traditional synthetic data generation methods often fail to address this problem effectively. A major limitation of these methods is that they t…

In most manufacturing sites, the data collected from dynamic equipment systems change over time owing to facility maintenance, environmental fluctuations, and aging equipment. Therefore, previously trained predictive models tend to perform worse as time passes…

Imbalanced data classification is a challenging problem frequently encountered in many real-world applications. Traditional classification algorithms are generally designed to maximize overall accuracy; therefore, their effectiveness tends to be impeded by imb…

Clustering is a subjective task, that is, several different results can be obtained from a single clustering hierarchy, depending on the observation scale. A local view of the data may necessitate more clusters, whereas a global view requires fewer clusters. I…

The naive Bayesian classification method has received significant attention in the field of supervised learning. This method has an unrealistic assumption in that it views all attributes as equally important. Attribute weighting is one of the methods used to a…

Since the coronavirus disease 2019 (COVID-19) pandemic, most professional sports events have been held without spectators. It is generally believed that home teams deprived of enthusiastic support from their home fans experience reduced benefits of playing on …

Imbalanced data classification is one of the most important tasks in the field of machine learning because abnormality, which is usually of our interest, appears less frequently than normality in real-world systems. Learning classifiers from imbalanced data ca…

Two-way cooperative collaborative filtering (CF) has been known to be crucial for binary market basket data. We propose an improved two-way logistic regression approach, a Pearson correlation-based score, a random forests (RF) R-square-based score, an RF Pears…

Although transmission electron microscopy (TEM) may be one of the most efficient techniques available for studying the morphological characteristics of nanoparticles, analyzing them quantitatively in a statistical manner is exceedingly difficult. Herein, we re…

The purpose of this study is to develop a prediction model that identifies the potential risk of fatality accidents at construction sites using machine learning based on industrial accident data collected by the Ministry of Employment and Labor (MOEL) of the R…