Explainable artificial intelligence (XAI) has received great interest in the recent decade, due
to its importance in critical application domains, such as self-driving cars, law, and …

Data visualization is a key tool in data mining for understanding big datasets. Many
visualization methods have been proposed, including the well-regarded state-of-the-art …

Abstract Machine learning models are increasingly being used in critical sectors, but their
black-box nature has raised concerns about accountability and trust. The field of explainable …

Feature extraction transforms high-dimensional data into a new subspace of lower
dimensionality while keeping the classification accuracy. Traditional algorithms do not …

Manifold learning techniques have become increasingly valuable as data continues to grow
in size. By discovering a lower-dimensional representation (embedding) of the structure of a …

Artificial intelligence methods are being increasingly applied across various domains, but
their often opaque nature has raised concerns about accountability and trust. In response …

Fuzzy systems are well suited for nonlinear modeling. They can be effectively used if their
structure and structure parameters are properly chosen. Moreover, it should be ensured that …

AI methods are finding an increasing number of applications, but their often black-box nature
has raised concerns about accountability and trust. The field of explainable artificial …

In this paper a new approach for construction of neuro-fuzzy systems for nonlinear
classification is introduced. In particular, we concentrate on the flexible neuro-fuzzy systems …

This paper studies the interpretability of transformations of labeled higher dimensional data
into a 2D representation (scatterplots) for visual classification. 1 In this context, the term …