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Complexity vs. Performance in Granular Embedding Spaces for Graph Classification

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World Applications, Financial Applications, Neural Prostheses and Medical Applications, Neural Based Data Mining and Complex Information Process; Learning Paradigms and Algorithms; Stochastic Learning and Statistical Algorithms

Authors: Luca Baldini ; Alessio Martino and Antonello Rizzi

Affiliation: Department of Information Engineering, Electronics and Telecommunications, University of Rome “La Sapienza”, via Eudossiana 18, 00184 Rome, Italy

ISBN: 978-989-758-475-6

Keyword(s): Structural Pattern Recognition, Supervised Learning, Embedding Spaces, Granular Computing, Graph Edit Distances.

Abstract: 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 can be convenient and/or necessary in many contexts, most of the state-of the art classification techniques can not be deployed directly in the graph domain without first embedding graph patterns towards a metric space. Granular Computing is a powerful information processing paradigm that can be employed in order to drive the synthesis of automatic embedding spaces from structured domains. In this paper we investigate several classification techniques starting from Granular Computing-based embedding procedures and provide a thorough overview in terms of model complexity, embedding space complexity and performances on several open-access datasets for graph classification. We witness that certain classification techniques perform poorly both from the point of view of complexity and learning p erformances as the case of non-linear SVM, suggesting that high dimensionality of the synthesized embedding space can negatively affect the effectiveness of these approaches. On the other hand, linear support vector machines, neuro-fuzzy networks and nearest neighbour classifiers have comparable performances in terms of accuracy, with second being the most competitive in terms of structural complexity and the latter being the most competitive in terms of embedding space dimensionality. (More)

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Paper citation in several formats:
Baldini, L.; Martino, A. and Rizzi, A. (2020). Complexity vs. Performance in Granular Embedding Spaces for Graph Classification.In Proceedings of the 12th International Joint Conference on Computational Intelligence - Volume 1: NCTA, ISBN 978-989-758-475-6, pages 338-349. DOI: 10.5220/0010109503380349

@conference{ncta20,
author={Luca Baldini. and Alessio Martino. and Antonello Rizzi.},
title={Complexity vs. Performance in Granular Embedding Spaces for Graph Classification},
booktitle={Proceedings of the 12th International Joint Conference on Computational Intelligence - Volume 1: NCTA,},
year={2020},
pages={338-349},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010109503380349},
isbn={978-989-758-475-6},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence - Volume 1: NCTA,
TI - Complexity vs. Performance in Granular Embedding Spaces for Graph Classification
SN - 978-989-758-475-6
AU - Baldini, L.
AU - Martino, A.
AU - Rizzi, A.
PY - 2020
SP - 338
EP - 349
DO - 10.5220/0010109503380349

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