Multi-layer incremental induction

  • Xindong Wu
  • William H. W. Lo
Induction (Improving Classifier’s Accuracy)
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1531)


This paper describes a multi-layer incremental induction algorithm, MLII, which is linked to an existing nonincremental induction algorithm to learn incrementally from noisy data. MLII makes use of three operations: data partitioning, generalization and reduction. Generalization can either learn a set of rules from a (sub)set of examples, or refine a previous set of rules. The latter is achieved through a redescription operation called reduction: from a set of examples and a set of rules, we derive a new set of examples describing the behaviour of the rule set. New rules are extracted from these behavioral examples, and these rules can be seen as meta-rules, as they control previous rules in order to improve their predictive accuracy. Experimental results show that MLII achieves significant improvement on the existing nonincremental algorithm HCV used for experiments in this paper, in terms of rule accuracy.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Xindong Wu
    • 1
  • William H. W. Lo
    • 1
  1. 1.School of Computer Science and Software EbgineeringMonash UniversityMelbourneAustralia

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