loading
Papers

Research.Publish.Connect.

Paper

Stochastic Information Granules Extraction for Graph Embedding and 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

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 and Italy

ISBN: 978-989-758-384-1

Keyword(s): Pattern Recognition, Supervised Learning, Granular Computing, Graph Embedding, Inexact Graph Matching.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: Graphs are data structures able to efficiently describe real-world systems and, as such, have been extensively used in recent years by many branches of science, including machine learning engineering. However, the design of efficient graph-based pattern recognition systems is bottlenecked by the intrinsic problem of how to properly match two graphs. In this paper, we investigate a granular computing approach for the design of a general purpose graph-based classification system. The overall framework relies on the extraction of meaningful pivotal substructures on the top of which an embedding space can be build and in which the classification can be performed without limitations. Due to its importance, we address whether information can be preserved by performing stochastic extraction on the training data instead of performing an exhaustive extraction procedure which is likely to be unfeasible for large datasets. Tests on benchmark datasets show that stochastic extraction can lead to a meaningful set of pivotal substructures with a much lower memory footprint and overall computational burden, making the proposed strategies suitable also for dealing with big datasets. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.172.217.40

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Baldini, L.; Martino, A. and Rizzi, A. (2019). Stochastic Information Granules Extraction for Graph Embedding and Classification.In Proceedings of the 11th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2019) ISBN 978-989-758-384-1, pages 391-402. DOI: 10.5220/0008149403910402

@conference{ncta19,
author={Luca Baldini. and Alessio Martino. and Antonello Rizzi.},
title={Stochastic Information Granules Extraction for Graph Embedding and Classification},
booktitle={Proceedings of the 11th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2019)},
year={2019},
pages={391-402},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008149403910402},
isbn={978-989-758-384-1},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2019)
TI - Stochastic Information Granules Extraction for Graph Embedding and Classification
SN - 978-989-758-384-1
AU - Baldini, L.
AU - Martino, A.
AU - Rizzi, A.
PY - 2019
SP - 391
EP - 402
DO - 10.5220/0008149403910402

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.