Realistic evaluation of uncertainty is crucial for informed decision-making in machine
learning (ML) models. Our study introduces a pioneering approach to quantify uncertainty in …

There are two main ways to construct Fuzzy Logic rule-based models: using expert
knowledge and using data mining methods. One of the most important aspects of Granular …

In this article, we introduce a neural network model for regression in which prediction
uncertainty is quantified by Gaussian random fuzzy numbers (GRFNs), a newly introduced …

As machine learning algorithms continue to advance, the incorporation of uncertainty
modeling becomes pivotal for robust and adaptable systems. This article explores the fusion …

Numeric models (including fuzzy models) produce numeric results. There are no ideal
models that deliver a complete match with the data. In this study, we advocate that a way of …

In recent years, granular fuzzy models have become an intensively studied category of fuzzy
models. Granular fuzzy models help elevate the existing models to the higher level of …

In this paper, the concept of prediction granules (PGs) is introduced for the real world
application problems. The PGs are constructed by prediction intervals (PIs) and a learning …

This paper proposes a new method for the formation of fuzzy higher type granular models.
This is accomplished by directly discovering uncertainty from a sample of numerical …

In the last few decades, there has been an increase in the interest of the scientific community
for multivariate statistical techniques of data analysis in which the data are affected by …

A new technique for forming information granules is shown in this chapter. Based on the
theory of uncertainty-based information, an approach is proposed which forms Interval Type …