Zaineb Dagdia BSc(2008), MSc(2010), PhD(2014)

Research Fellow

Contact Details

Room Number..........:  E51
Building....................:  Llandinam
Phone.......................:   +44 (0)1970 622410
E-Mail........................:   zac4
Home Page...............:   Personal

External Publication Websites:
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Zaineb Chelly Dagdia is a computer scientist who, thanks to her Marie-Skłodowska-Curie Individual European Fellowship, is currently developing her research on an optimized framework for Big Data pre-processing in certain and imprecise contexts at Aberystwyth University, Wales, UK.

Her research interests include different aspects of Artificial Intelligence. She writes on evolutionary algorithms and artificial immune systems (AIS). She deals with reasoning under uncertainty and focuses on developing new AIS methods within an imprecise framework based on machine learning techniques and mathematical theories. She also extended her domain of expertise by dealing with Big Data. Her career publications include 4 journal papers (SCIindexed), 19 refereed and ranked conference proceedings papers and 2 book chapters (Springer). She was awarded the IEEE Young Researcher First Price, the ACM-W Award and last but not least the Marie-Skłodowska-Curie Individual European Fellowship.

Research Groups

Research Interests

  • RoSTBiDFramework [Project Website]:

    Over the last decades, the amount of data has increased in an unprecedented rate, leading to a new terminology: "Big Data". Big data are specified by their Volume, Variety, Velocity and by their Veracity/Imprecision. Based on these 4V specificities, it has become difficult to quickly acquire the most useful information from the huge amount of data at hand. Thus, it is necessary to perform data (pre-)processing as a first step. In spite of the existence of many techniques for this task, most of the state-of-the-art methods require additional information for thresholding and are neither able to deal with the big data veracity aspect nor with their computational requirements. This project's overarching aim is to fill these major research gaps with an optimised framework for big data pre-processing in certain and imprecise contexts. Our approach is based on Rough Set Theory (RST) for data pre-processing and Randomised Search Heuristics for optimisation and will be implemented under the Spark MapReduce model. The project combines the expertise of the experienced researcher Dr Zaineb Chelly Dagdia in machine learning, rough set theory and information extraction with the knowledge in optimisation and randomised search heuristics of the supervisor Dr Christine Zarges at Aberystwyth University. Further expertise is provided by internal and external collaborators from academic and non-academic institutions, namely Prof Lebbah (University of Paris 13), Prof Shen (University of Aberystwyth), Prof Tino (University of Birmingham), Prof Merelo (University of Granada) and an industrial partner from France.


A Hybrid Fuzzy Maintained Classification Method Based on Dendritic CellsChelly Dagdia, Z. & Elouedi, Z. 2018 (Accepted/In press) In : Journal of Classification.
A Distributed Dendritic Cell Algorithm for Big DataChelly Dagdia, Z. 2018 (Accepted/In press) The Genetic and Evolutionary Computation Conference: GECCO.Association for Computing Machinery
Optimized Framework based on Rough Set Theory for Big Data Pre-processing in Certain and Imprecise Contexts” -- Marie Sklodowska-Curie Project: Open Problems’Chelly Dagdia, Z. 2018
Optimized Framework based on Rough Set Theory for Big Data Preprocessing in Certain and Imprecise ContextsChelly Dagdia, Z. 2018
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 massivesChelly Dagdia, Z., Zarges, C., Beck, G. & Lebbah, M. 2018 p. 377-3782 p.
Modèle de Sélection de Caractéristiques pour les Données Massives: Méthode de sélection de caractéristiques pour les données massivesChelly Dagdia, Z., Zarges, C., Beck, G. & Lebbah, M. 2018 15ème édition de l'atelier Fouille de Données Complexes: FDC.
A Distributed Rough Set Theory based Algorithm for an Efficient Big Data Pre-processing under the Spark FrameworkChelly Dagdia, Z., Zarges, C., Beck, G. & Lebbah, M. 2018 2017 IEEE International Conference on Big Data (Big Data). Nie, J-Y., Obradovic, Z., Suzumura, T., Ghosh, R., Nambiar, R., Wang, C., Zang, H., Baeza-Yates, R., Hu, X., Kepner, J., Cuzzocrea, A., Tang, J. & Toyoda, M. (eds.). IEEE Press, p. 911-916