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: Multilabel Classification
    sub title: Problem Analysis, Metrics and Techniques
  name:
    name part: Herrera, Francisco.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Charte, Francisco.
    role:
      role term: author.
  name:
    name part: Rivera, Antonio J.
    role:
      role term: author.
  name:
    name part: del Jesus, María J.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2016
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 194 p. 72 illus. online resource.
  abstract: This book offers a comprehensive review of multilabel techniques widely used to classify and label texts, pictures, videos and music in the Internet. A deep review of the specialized literature on the field includes the available software needed to work with this kind of data. It provides the user with the software tools needed to deal with multilabel data, as well as step by step instruction on how to use them. The main topics covered are: • The special characteristics of multi-labeled data and the metrics available to measure them. • The importance of taking advantage of label correlations to improve the results. • The different approaches followed to face multi-label classification. • The preprocessing techniques applicable to multi-label datasets. • The available software tools to work with multi-label data. This book is beneficial for professionals and researchers in a variety of fields because of the wide range of potential applications for multilabel classification. Besides its multiple applications to classify different types of online information, it is also useful in many other areas, such as genomics and biology. No previous knowledge about the subject is required. The book introduces all the needed concepts to understand multilabel data characterization, treatment and evaluation.
  table of contents: Introduction -- Multilabel Classification -- Case Studies and Metrics -- Transformation based Classifiers -- Adaptation based Classifiers -- Ensemble based Classifiers -- Dimensionality Reduction -- Imbalance in Multilabel Datasets -- Multilabel Software.
  note: by Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus.
  subject:
    topic: Computer science
  subject:
    topic: Data mining
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computer Science
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  classification: QA76.9.D343
  classification: 006.312
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783319411118
  identifier: http://dx.doi.org/10.1007/978-3-319-41111-8
  record info:
    record creation date: 160809
    record change date: 20160809085244.0
    record identifier: 978-3-319-41111-8