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

Research output

Research output per year 2018 2018 2021

• 2 Conference Proceeding (Non-Journal item)
• 2 Article
• 1 Chapter
• 1 Paper

Research output per year

Research output per year

Search results

• 2021

  A Detailed Study of the Distributed Rough Set Based Locality Sensitive Hashing Feature Selection Technique

  Chelly Dagdia, Z. & Zarges, C., 30 Sept 2021, In: Fundamenta Informaticae. 182, 2, p. 111-179 69 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Locality Sensitive Hashing 100%
  • Rough Set Theory 87%
  • Partitioning 37%
  • Feature Selection 37%
  • Search Space 25%
  199 Downloads (Pure)
• 2020

  A Scalable and Effective Rough Set Theory based Approach for Big Data Pre-processing

  Chelly Dagdia, Z., Zarges, C., Beck, G. & Lebbah, M., 01 Aug 2020, In: Knowledge and Information Systems. 62, 8, p. 3321-3386 66 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

  • Rough Set Theory 100%
  • Data Preprocessing 75%
  • Feature Selection 75%
  • Big Data 50%
  • Biggest Challenge 25%
  17 Citations (Scopus)

  150 Downloads (Pure)
• 2019

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

  Chelly Dagdia, Z., Zarges, C., Schannes, B., Micalef, M., Galiana, L., Rolland, B., de Fresnoye, O. & Benchoufi, M., 18 Jan 2019, Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Proceedings. Brefeld, U., Marascu, A., Pinelli, F., Curry, E., MacNamee, B., Hurley, N., Daly, E. & Berlingerio, M. (eds.). Springer Nature, p. 440-455 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11053 LNAI).

  Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

  Open Access

  File

  • Rough Set Theory 100%
  • Case Study 100%
  • Real World 50%
  • Biggest Challenge 50%
  • Data Preprocessing 50%
  5 Citations (Scopus)

  351 Downloads (Pure)
• 2018

  A Distributed Rough Set Theory based Algorithm for an Efficient Big Data Pre-processing under the Spark Framework

  Chelly Dagdia, Z., Zarges, C., Beck, G. & Lebbah, M., 15 Jan 2018, Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. 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 6 p. (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017; vol. 2018-January).

  Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

  Open Access

  File

  • Rough Set Theory 100%
  • Big Data 60%
  • Feature Selection 60%
  • Data Preprocessing 40%
  • Algorithms 40%
  16 Citations (Scopus)

  290 Downloads (Pure)

• Modèle de Sélection de Caractéristiques pour les Données Massives: Méthode de sélection de caractéristiques pour les données massives

  Chelly Dagdia, Z., Zarges, C., Beck, G. & Lebbah, M., 23 Jan 2018, 15ème édition de l'atelier Fouille de Données Complexes: FDC.

  Research output: Chapter in Book/Report/Conference proceeding › Chapter

  Open Access

  File
  71 Downloads (Pure)

• 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

  Chelly Dagdia, Z., Zarges, C., Beck, G. & Lebbah, M., 26 Jan 2018, p. 377-378. 2 p.

  Research output: Contribution to conference › Paper › peer-review

  Open Access

  File
  48 Downloads (Pure)