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Scope classification: An instance-based learning algorithm with a rule-based characterisation

  • Nicolas Lachiche
  • Pierre Marquise
Instance Based Learning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1398)

Abstract

Scope classification is a new instance-based learning (IBL) technique with a rule-based characterisation. Within the scope approach, the classification of an object o is based on the examples that are closer to o than every example labelled with another class. In contrast to standard distance-based IBL classifiers, scope classification relies on partial preorderings ≤o between examples, indexed by objects. Interestingly, the notion of closeness to o that is used characterises the classes predicted by all the rules that cover o and are relevant and consistent for the training set. Accordingly, scope classification is an IBL technique with a rule-based characterisation. Since rules do not have to be explicitly generated, the scope approach applies to classification problems where the number of rules prevents them from being exhaustively computed.

Keywords

Execution Time Learning Phase Consistent Rule Simple Majority Vote Scope Approach 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Nicolas Lachiche
    • 1
  • Pierre Marquise
    • 2
  1. 1.LORIAVandoeuvre-lès-Nancy CedexFrance
  2. 2.GRIL/Université d'Artois, Rue de l'UniversitéLens CedexFrance

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