Meta-classifiers and Selective Superiority View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2003-04-18

AUTHORS

Ryan Benton , Miroslav Kubat , Rasaiah Loganantharaj

ABSTRACT

Given that no one classification method is the best in all tasks, a variety of approaches have evolved to prevent poor performance due to mismatch of capabilities. One approach to overcome this problem is to determine when a method may be appropriate for a given problem. A second, more popular approach is to combine the capabilities of two or more classification methods. This paper provides some evidence that the combining of classifiers can yield more robust solutions. More... »

PAGES

434-442

References to SciGraph publications

  • 1991-01. Instance-based learning algorithms in MACHINE LEARNING
  • 1990-08. Empirical learning as a function of concept character in MACHINE LEARNING
  • 1999-07. Using Correspondence Analysis to Combine Classifiers in MACHINE LEARNING
  • 1995-07. Recursive automatic bias selection for classifier construction in MACHINE LEARNING
  • 1995. Characterization of classification algorithms in PROGRESS IN ARTIFICIAL INTELLIGENCE
  • 1986-03. Induction of decision trees in MACHINE LEARNING
  • 1990-06. The strength of weak learnability in MACHINE LEARNING
  • Book

    TITLE

    Intelligent Problem Solving. Methodologies and Approaches

    ISBN

    978-3-540-67689-8
    978-3-540-45049-8

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/3-540-45049-1_53

    DOI

    http://dx.doi.org/10.1007/3-540-45049-1_53

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1003220148


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