Network traffic analysis, and specifically anomaly and attack detection, call for sophisticated
tools relying on a large number of features. Mathematical modeling is extremely difficult …

Granular Computing is a powerful information processing paradigm, particularly useful for
the synthesis of pattern recognition systems in structured domains (eg, graphs or …

Embedding spaces are one of the mainstream approaches when dealing with structured
data. Granular Computing, in the last decade, emerged as a powerful paradigm for the …

Granular Computing is a powerful information processing paradigm for synthesizing
advanced pattern recognition systems in non-conventional domains. In this article, a novel …

The most fascinating aspect of graphs is their ability to encode the information contained in
the inner structural organization between its constituting elements. Learning from graphs …

Graph kernels are one of the mainstream approaches when dealing with measuring
similarity between graphs, especially for pattern recognition and machine learning tasks. In …

Graph embedding is an established and popular approach when designing graph-based
pattern recognition systems. Amongst the several strategies, in the last ten years, Granular …

The most distinctive trait in structural pattern recognition in graph domain is the ability to deal
with the organization and relations between the constituent entities of the pattern. Even if this …

The synthesis of a pattern recognition system usually aims at the optimization of a given
performance index. However, in many real-world scenarios, there exist other desired facets …

Graphs have gained a lot of attention in the pattern recognition community thanks to their
ability to encode both topological and semantic information. Despite their invaluable …