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Improved Knowledge Mining with the Multimethod Approach

  • Mitja Lenič
  • Peter Kokol
  • Milan Zorman
  • Petra Povalej
  • Bruno Stiglic
  • Ryuichi Yamamoto
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 6)

Abstract

Automatic induction from examples has a long tradition and represents an important technique used in data mining. Trough induction a method builds a hypothesis to explain observed facts. Many knowledge extraction methods have been developed, unfortunately each has advantages and limitations and in general there is no such method that would outperform all others on all problems. One of the possible approaches to overcome this problem is to combine different methods in one hybrid method. Recent research is mainly focused on a specific combination of methods, contrary, multimethod approach combines different induction methods in an unique manner – it applies different methods on the same knowledge model in no predefined order where each method may contain inherent limitations with the expectation that the combined multiple methods may produce better results. In this paper we present the overview of an idea, concrete integration and possible improvements.

Keywords

Support Vector Machine Decision Tree Knowledge Representation Population Operator Knowledge Extraction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Authors and Affiliations

  • Mitja Lenič
    • 1
    • 2
  • Peter Kokol
    • 1
    • 2
  • Milan Zorman
    • 1
    • 2
  • Petra Povalej
    • 1
    • 2
  • Bruno Stiglic
    • 1
    • 2
  • Ryuichi Yamamoto
    • 1
    • 2
  1. 1.Laboratory for system design, Faculty of Electrical Engineering and Computer ScienceUniversity of MariborMaribor
  2. 2.Division of Medical InformaticsOsaka Medical CollegeTakatsuki City, Osaka

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