While anomaly detection is relatively well-studied, it remains a topic of ongoing interest and
challenge, as our society becomes increasingly interconnected and digitalized. In this paper …

In the last decades, a wide portfolio of Feature Weighting (FW) methods have been
proposed in the literature. Their main potential is the capability to transform the features in …

Class imbalance is one of the most popular and important issues in the domain of
classification. The AdaBoost algorithm is an effective solution for classification, but it still …

Abstract The Synthetic Minority Over-Sampling Technique (SMOTE) is one of the most well
known methods to solve the unequal class distribution problem in imbalanced datasets …

Naive Bayes (NB) continues to be one of the top 10 data mining algorithms, but its
conditional independence assumption rarely holds true in real-world applications …

Pandemic events, particularly the current Covid-19 disease, compel organisations to re-
formulate their day-to-day operations for achieving various business goals such as cost …

In real-world binary classification problems, the entirety of samples belonging to each class
varies. These types of problems where the majority class is notably bigger than the minority …

Highly imbalanced class distribution has been well-recognized as a major cause of
performance degradation for most supervised learning algorithms. Unfortunately, such …

Broad learning system (BLS) is a novel and efficient model, which facilitates representation
learning and classification by concatenating feature nodes and enhancement nodes. In spite …

Data imbalance is a thorny issue in machine learning. SMOTE is a famous oversampling
method of imbalanced learning. However, it has some disadvantages such as sample …