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

Project: Externally funded research

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

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  • 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
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    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
    16 Citations (Scopus)
    289 Downloads (Pure)