Currently, knowledge discovery in databases is an essential first step when identifying valid,
novel and useful patterns for decision making. There are many real-world scenarios, such as …

Feature selection methods with antinoise performance are effective dimensionality reduction
methods for classification tasks with noise. However, there are few studies on robust feature …

Since the outbreak of the financial crisis, the major global credit rating agencies have
implemented significant changes to their methodologies to assess the sovereign credit risk …

Ordinal classification is a common classification problem, which widely exists in multi-
attribute decision making problems. The dominance-based rough set approach (DRSA) is a …

Monotonic classification is a common task in the field of multicriteria decision-making, in
which features and decision obey a monotonic constraint. The dominance-based rough set …

As a powerful knowledge mining technique for ordinal classification tasks, dominance-
based rough set theory has many advantages but also some issues. Sensitivity to noisy …

Instance selection aims to search for the best patterns in the training set and main instance
selection methods include condensation methods, edition methods and hybrid methods …

In real-life applications, monotonic classification is a widespread task, where the
improvement of a particular input value cannot result in an inferior output. A common …

This paper presents, for the first time, a distance metric learning algorithm for monotonic
classification. Monotonic datasets arise in many real-world applications, where there exist …

This paper proposes a new model based on Fuzzy k-Nearest Neighbors for classification
with monotonic constraints, Monotonic Fuzzy k-NN (MonFkNN). Real-life data-sets often do …