The ability to understand and explain the outcomes of data analysis methods, with regard to
aiding decision-making, has become a critical requirement for many applications. For …

Predictive models are increasingly being used to optimize decision-making and minimize
costs. A conventional approach is predict-then-optimize: first, a predictive model is built; …

Anomaly detection approaches have become critically important to enhance decision-
making systems, especially regarding the process of risk reduction in the economic …

Class imbalance learning is one of the most important topics in the field of machine learning
and data mining, and the Synthetic Minority Oversampling Techniques (SMOTE) is the …

In recent years, most researchers focused on the classification problems of imbalanced data
sets, and these problems are widely distributed in industrial production and medical …

Individual treatment effect models allow optimizing decision-making by predicting the effect
of a treatment on an outcome of interest for individual instances. These predictions allow …

The supplier-customer relationships in the supply chain reflect the transaction activities
between companies, which can also imply the relationships across the financial data …

Fraud detection in bank payments transactions suffers from a high number of false positives.
To deal with this problem, we introduce a rules generation framework for a fraud-detection …

The least squares support vector machine (LSSVM) has achieved great success in various
fields, but it still has certain limitations. Firstly, it treats all points equally and does not take …

The purpose of this paper is to enhance current practices in business-to-business (B2B)
customer churn prediction modelling. Following the recent trend from accuracy-based to …