rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Evolutionary Decision Trees in Large-Scale Data Mining
  name:
    name part: Kretowski, Marek.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XI, 180 p. 69 illus. online resource.
  abstract: This book presents a unified framework, based on specialized evolutionary algorithms, for the global induction of various types of classification and regression trees from data. The resulting univariate or oblique trees are significantly smaller than those produced by standard top-down methods, an aspect that is critical for the interpretation of mined patterns by domain analysts. The approach presented here is extremely flexible and can easily be adapted to specific data mining applications, e.g. cost-sensitive model trees for financial data or multi-test trees for gene expression data. The global induction can be efficiently applied to large-scale data without the need for extraordinary resources. With a simple GPU-based acceleration, datasets composed of millions of instances can be mined in minutes. In the event that the size of the datasets makes the fastest memory computing impossible, the Spark-based implementation on computer clusters, which offers impressive fault tolerance and scalability potential, can be applied.
  table of contents: Evolutionary computation -- Decision trees in data mining -- Parallel and distributed computation -- Global induction of univariate trees -- Oblique and mixed decision trees -- Cost-sensitive tree induction -- Multi-test decision trees for gene expression data -- Parallel computations for evolutionary induction.
  note: by Marek Kretowski.
  subject:
    topic: Engineering-Data processing
  subject:
    topic: Computational intelligence
  subject:
    topic: Big data
  subject:
    topic: Data Engineering
  subject:
    topic: Computational Intelligence
  subject:
    topic: Big Data
  classification: TA345-345.5
  classification: 620.00285
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Studies in Big Data, 59
  identifier: 9783030218515
  identifier: https://doi.org/10.1007/978-3-030-21851-5
  record info:
    record creation date: 190605
    record change date: 20191022041815.0
    record identifier: 978-3-030-21851-5