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Classifiers Based on Two-Layered Learning

  • Jan G. Bazan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3066)

Abstract

In this paper we present an exemplary classifier (classification algorithm) based on two-layered learning. In the first layer of learning a collection of classifiers is induced from a part of original training data set. In the second layer classifiers are induced using patterns extracted from already constructed classifiers on the basis of their performance on the remaining part of training data. We report results of experiments performed on the following data sets, well known from literature: diabetes, heart disease, australian credit (see [5]) and lymphography (see [4]). We compare the standard rough set method used to induce classifiers (see [1] for more details), based on minimal consistent decision rules (see [6]), with the classifier based on two-layered learning.

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jan G. Bazan
    • 1
  1. 1.Institute of MathematicsUniversity of RzeszówRzeszówPoland

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