Uncertainty and imprecision accompany the world we live in and occur in almost every
event. How to better interpret and manage uncertainty and imprecision play a vital role in …

Rough set theory and belief function theory, two popular mathematical frameworks for
uncertainty representation, have been widely applied in different settings and contexts …

Understanding the uncertainty involved in a mass function is a central issue in Dempster–
Shafer evidence theory for uncertain information fusion. Recent advances suggest to …

We propose a new classifier based on Dempster-Shafer (DS) theory and a convolutional
neural network (CNN) architecture for set-valued classification. In this classifier, called the …

Distress segmentation assigns each pixel of a pavement image to one distress class or
background, which provides a simplified representation for distress detection and …

In the new era of Artificial Intelligence (AI), Generative Pre-Trained Transformer (GPT) has
emerged as a central technique for generating human-like texts. Over recent years, there …

Single-modality medical images generally do not contain enough information to reach an
accurate and reliable diagnosis. For this reason, physicians commonly rely on multimodal …

It is still a challenging problem to characterize uncertainty and imprecision between specific
(singleton) clusters with arbitrary shapes and sizes. In order to solve such a problem, we …

In applications of domain adaptation, there may exist multiple source domains, which can
provide more or less complementary knowledge for pattern classification in the target …

Classifier fusion remains an effective method to improve classification performance. In
applications, the classifiers learnt using different attributes may work with various frames of …