Optimised Framework based on Rough Set Theory for Big Data Pre-processing in Certain and Imprecise Contexts

Project Information

RoSTBiDFramework

Grant agreement ID: 702527

DOI

10.3030/702527

Project closed

EC signature date 31 March 2016

Start date 1 March 2017

End date 28 February 2019

Funded under

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Total cost

€ 183 454,80

EU contribution

€ 183 454,80

Coordinated by

ABERYSTWYTH UNIVERSITY
United Kingdom

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Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Publications

Conference proceedings (8)

A distributed rough set theory based algorithm for an efficient big data pre-processing under the spark framework

Author(s): Zaineb Chelly Dagdia, Christine Zarges, Gael Beck, Mustapha Lebbah
Published in: 2017 IEEE International Conference on Big Data (Big Data), 2017, Page(s) 911-916, ISBN 978-1-5386-2715-0
Publisher: IEEE
DOI: 10.1109/BigData.2017.8258008

Optimized Framework based on Rough Set Theory for Big Data Preprocessing in Certain and Imprecise Contexts

Author(s): Zaineb Chelly Dagdia
Published in: The 5th MCAA Annual Conference and General Assembly, 2018
Publisher: MCAA Annual Conference and General Assembly

Optimized Framework based on Rough Set Theory for Big Data Pre-processing in Certain and Imprecise Contexts” -- Marie Sklodowska-Curie Project: Open Problems’

Author(s): Zaineb Chelly Dagdia
Published in: Recent Trends in Knowledge Compilation (Dagstuhl Seminar 17381), 2018
Publisher: Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik
DOI: 10.4230/DagRep.7.9.62

Nouveau Modèle de Sélection de Caractéristiques basé sur la Théorie des Ensembles Approximatifs pour les Données Massives : Méthode de sélection de caractéristiques pour les données massives

Author(s): Zaineb Chelly Dagdia, Christine Zarges, Gael Beck, Mustapha Lebbah
Published in: Conférence Internationalle sur l'Extraction et la Gestion des Connaissances, 2018, Page(s) 377--378, ISBN 979-10-96289-07-3
Publisher: Revue des Nouvelles Technologies de l'Information

Modèle de Sélection de Caractéristiques pour les Données Massives

Author(s): Zaineb Chelly Dagdia, Christine Zarges, Gael Beck, Mustapha Lebbah
Published in: 15ème édition de l'atelier Fouille de Données Complexes, 2018, Page(s) 1--12
Publisher: Revue des Nouvelles Technologie de l'Information

A distributed dendritic cell algorithm for big data

Author(s): Zaineb Chelly Dagdia
Published in: Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '18, 2018, Page(s) 103-104, ISBN 9781-450357647
Publisher: ACM Press
DOI: 10.1145/3205651.3205701

Distributed Rough Set Based Feature Selection Approach to Analyse Deep and Hand-crafted Features for Mammography Mass Classification

Author(s): Azam Hamidinekoo, Zaineb Chelly Dagdia, Zobia Suhail, Reyer Zwiggelaar
Published in: 2018 IEEE International Conference on Big Data (Big Data), 2018, Page(s) 2423-2432, ISBN 978-1-5386-5035-6
Publisher: IEEE
DOI: 10.1109/bigdata.2018.8621962

A Distributed Rough Set Theory Algorithm based on Locality Sensitive Hashing for an Efficient Big Data Pre-processing

Author(s): Zaineb Chelly Dagdia, Christine Zarges, Gael Beck, Hanene Azzag, Mustapha Lebbah
Published in: 2018 IEEE International Conference on Big Data (Big Data), 2018, Page(s) 2597-2606, ISBN 978-1-5386-5035-6
Publisher: IEEE
DOI: 10.1109/bigdata.2018.8622024

Peer reviewed articles (2)

A Hybrid Fuzzy Maintained Classification Method Based on Dendritic Cells

Author(s): Zaineb Chelly Dagdia and Zied Elouedi
Published in: Journal of Classification, 2019, ISSN 1432-1343
Publisher: Springer US

A scalable and distributed dendritic cell algorithm for big data classification

Author(s): Zaineb Chelly Dagdia
Published in: Swarm and Evolutionary Computation, 2018, Page(s) 1-13, ISSN 2210-6502
Publisher: Elsevier BV
DOI: 10.1016/j.swevo.2018.08.009

Book chapters (1)

Rough Set Theory as a Data Mining Technique: A Case Study in Epidemiology and Cancer Incidence Prediction

Author(s): Zaineb Chelly Dagdia, Christine Zarges, Benjamin Schannes, Martin Micalef, Lino Galiana, Benoît Rolland, Olivier de Fresnoye, Mehdi Benchoufi
Published in: Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part III, Issue 11053, 2019, Page(s) 440-455, ISBN 978-3-030-10996-7
Publisher: Springer International Publishing
DOI: 10.1007/978-3-030-10997-4_27