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Soft Computing
Erschienen in: Soft Computing 2/2014

01.02.2014 | Methodologies and Application

A Granular Computing approach to the design of optimized graph classification systems

verfasst von: Filippo Maria Bianchi, Lorenzo Livi, Antonello Rizzi, Alireza Sadeghian

Erschienen in: Soft Computing | Ausgabe 2/2014

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Abstract

Research on Graph-based pattern recognition and Soft Computing systems has attracted many scientists and engineers in several different contexts. This fact is motivated by the reason that graphs are general structures able to encode both topological and semantic information in data. While the data modeling properties of graphs are of indisputable power, there are still different concerns about the best way to compute similarity functions in an effective and efficient manner. To this end, suited transformation procedures are usually conceived to address the well-known Inexact Graph Matching problem in an explicit embedding space. In this paper, we propose two graph embedding algorithms based on the Granular Computing paradigm, which are engineered as key procedures of a general-purpose graph classification system. Tests have been conducted on benchmarking datasets relying on both synthetic and real-world data, achieving competitive results in terms of test set classification accuracy.
Vorheriger Artikel A study in facial regions saliency: a fuzzy measure approach

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Metadaten
Titel
A Granular Computing approach to the design of optimized graph classification systems
verfasst von
Filippo Maria Bianchi
Lorenzo Livi
Antonello Rizzi
Alireza Sadeghian
Publikationsdatum
01.02.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 2/2014
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-013-1065-z

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