Hierarchical Data Topology Based Selection for Large Scale Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Hmida:2016:SmartWorld,
  author =       "Hmida Hmida and Sana Ben Hamida and Amel Borgi and 
                 Marta Rukoz",
  booktitle =    "2016 Intl IEEE Conferences on Ubiquitous Intelligence
                 Computing, Advanced and Trusted Computing, Scalable
                 Computing and Communications, Cloud and Big Data
                 Computing, Internet of People, and Smart World Congress
                 (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld)",
  title =        "Hierarchical Data Topology Based Selection for Large
                 Scale Learning",
  year =         "2016",
  pages =        "1221--1226",
  abstract =     "The amount of available data for data mining,
                 knowledge discovery continues to grow very fast with
                 the era of Big Data. Genetic Programming algorithms
                 (GP), that are efficient machine learning techniques,
                 are face up to a new challenge that is to deal with the
                 mass of the provided data. Active Sampling, already
                 used for Active Learning, might be a good solution to
                 improve the Evolutionary Algorithms (EA) training from
                 very big data sets. This paper investigates the
                 adaptation of Topology Based Selection (TBS) to face
                 massive learning datasets by means of Hierarchical
                 Sampling. We propose to combine the Random Subset
                 Selection (RSS) with the TBS to create the RSS-TBS
                 method. Two variants are implemented, applied to solve
                 the KDD intrusion detection problem. They are compared
                 to the original RSS, TBS techniques. The experimental
                 results show that the important computational cost
                 generated by original TBS when applied to large
                 datasets can be lightened with the Hierarchical
                 Sampling.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0186",
  month =        jul,
  notes =        "Also known as \cite{7816982}",
}

Genetic Programming entries for Hmida Hmida Sana Ben Hamida Amel Borgi Marta Rukoz

Citations