Learning algorithms can be less effective on datasets with an extensive feature space due to
the presence of irrelevant and redundant features. Feature selection is a technique that …

Emotions are a critical aspect of daily life and serve a crucial role in human decision-making,
planning, reasoning, and other mental states. As a result, they are considered a significant …

Class imbalance occurs when the class distribution is not equal. Namely, one class is under-
represented (minority class), and the other class has significantly more samples in the data …

Imbalanced learning constitutes one of the most formidable challenges within data mining
and machine learning. Despite continuous research advancement over the past decades …

Recent progress in engineering highly promising biocatalysts has increasingly involved
machine learning methods. These methods leverage existing experimental and simulation …

While the opportunities of ML and AI in healthcare are promising, the growth of complex data-
driven prediction models requires careful quality and applicability assessment before they …

Advances in microscopy hold great promise for allowing quantitative and precise
measurement of morphological and molecular phenomena at the single-cell level in …

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key
equipment systems. Ensemble learning integrates different weak learning methods to obtain …

The class imbalance problem is common in the credit scoring domain, as the number of
defaulters is usually much less than the number of non-defaulters. To date, research on …

Abstract Distributed Denial of Service (DDoS) attack is one of the biggest cyber threats.
DDoS attacks have evolved in quantity and volume to evade detection and increase …