The imbalanced learning issue is related to the performance of learning algorithms in the
presence of asymmetrical class distribution. Due to the complex characteristics of …

In recent years, an enormous amount of research has been carried out on support vector
machines (SVMs) and their application in several fields of science. SVMs are one of the …

Class imbalance poses new challenges when it comes to classifying data streams. Many
algorithms recently proposed in the literature tackle this problem using a variety of data …

Many real-world data-mining applications involve obtaining predictive models using
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …

Incorporating phase change materials (PCMs) into cementitious composites has recently
attracted paramount interest. While it can enhance thermal characteristics and energy …

The random forest (RF) algorithm is an ensemble of classification or regression trees and is
widely used, including for species distribution modelling (SDM). Many researchers use …

A decision tree is a predictive model that recursively partitions the covariate's space into
subspaces such that each subspace constitutes a basis for a different prediction function …

Classifier learning with data-sets that suffer from imbalanced class distributions is a
challenging problem in data mining community. This issue occurs when the number of …

Billions of dollars of loss are caused every year due to fraudulent credit card transactions.
The design of efficient fraud detection algorithms is key for reducing these losses, and more …

In recent years, monitoring the health condition of existing bridges has become a common
requirement. By providing an information management system, Bridge Information Model …