With the recent successes of black-box models in Artificial Intelligence (AI) and the growing
interactions between humans and AIs, explainability issues have risen. In this article, in the …

The purpose of this paper is to develop a linear programming technique for
multidimensional analysis of preference (LINMAP) to address multiple criteria decision …

The learning algorithm for multivariable data analysis (LAMDA) is a learning method to
group or classify quantitative and qualitative historical data. LAMDA can be applied for self …

Prototypes, as Rosch (1973) defined the term in the cognitive sciences field, are ideal
exemplars that summarize and represent groups of objects (or categories) and that are …

In the spirit of explainable AI approaches, this paper introduces a new strategy whose aim is
to linguistically describe the inner structure of a dataset. Instead of removing irregular points …

Fuzzy set theory offers an important contribution to data mining leading to fuzzy data mining.
It enables the management of interpretable and subjective information in both input and …

In this paper, we describe various aspects of Explainable Artificial Intelligence (XAI) and we
show that fuzzy systems can help to approach several of these aspects. We focus on the …

The learning algorithm and multivariable data analysis (LAMDA) is an algorithm to group
quantitative and qualitative data, applying self-learning and/or directed learning. Usually …

ABSTRACT Purpose The United Kingdom's European Union membership referendum,
which marked the end of UK membership in the European Union, created much uncertainty …

On the one hand, clustering methods are of a particular interest to automatically identify the
inner structure of a data set. On the other hand, fuzzy partitions are particularly suitable to …