Two Steps Genetic Programming for Big Data - Perspective of Distributed and High-Dimensional Data

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

@InProceedings{Huang:2015:ieeeBigData,
  author =       "Jih-Jeng Huang",
  booktitle =    "2015 IEEE International Congress on Big Data",
  title =        "Two Steps Genetic Programming for Big Data -
                 Perspective of Distributed and High-Dimensional Data",
  year =         "2015",
  pages =        "753--756",
  abstract =     "The term big data has been the most popular topic in
                 recent years in practice, academe and government for
                 realizing the value of data. Then, many information
                 technologies and software are proposed to deal with big
                 data, such as Hadoop, NoSQL databases, and cloud
                 computing. However, these tools can only help us to
                 store, manage, search, and control data rather than
                 extract knowledge from big data. The only way to mine
                 the nugget from big data is to have the ability to
                 analyse them. The characteristics of complexity of big
                 data, e.g., Volume and variety make traditional data
                 mining algorithms invalid. In this paper, we deal with
                 big data by solving distributed and high-dimensional
                 problems. We propose a novel algorithm to effectively
                 extract knowledge from big data. According to the
                 empirical study, the propose method can handle big data
                 soundly.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/BigDataCongress.2015.125",
  ISSN =         "2379-7703",
  month =        jun,
  notes =        "Also known as \cite{7207309}",
}

Genetic Programming entries for Jih-Jeng Huang

Citations