Learning from imbalanced datasets is a nontrivial task for supervised learning community.
Traditional classifiers may have difficulties to learn the concept related to the minority class …

The fundamental goal of group decision making (GDM) is to improve consensus amongst
experts and reduce individual conflicts of interest in the process of alternative selection. By …

Financial inclusion aims to provide financial services at an affordable cost to low-income
groups in need. However, the lack of effective credit evaluation information for such groups …

Wetlands are among the most productive natural ecosystems globally, providing crucial
ecosystem services to people. Regrettably, a substantial 64%–71% of wetlands have been …

With the popularity of online transactions, credit card fraud incidents are occurring more and
more frequently, and adaptive enhancement (Adaboost) models are most often used in …

The synthetic minority oversampling technique (SMOTE) is the most general and popular
solution for imbalanced data. Although SMOTE is effective in solving the class imbalance …

In classification tasks with large sample sets, the use of a single classifier carries the risk of
overfitting. To overcome this issue, an ensemble of classifier models has often been shown …

The current study evaluates the financial innovation life cycle for renewable energy
investments. A novel model is proposed that has two stages. First, the financial innovation …

The multi-class imbalance problem has a higher level of complexity when compared to the
binary class problem. The difficulty is due to the large number of classes that will present …

This study presents a new linear multivariate decision tree (MDT) algorithm that incorporates
linear programming and components of the branch-and-bound methodology, such as bound …