Rough-Set-Inspired Feature Subset Selection, Classifier Construction, and Rule Aggregation

  • Dominik Ślęzak
  • Sebastian Widz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6954)


We consider a rough-set-inspired framework for deriving feature subset ensembles from data. Each of feature subsets yields a single classifier, basically by generating its corresponding if-then decision rules from the training data. Feature subsets are extracted according to a simple randomized algorithm, following the filter (rather than wrapper or embedded) methodology. Classifier ensemble is built from single classifiers by defining aggregation laws on top of decision rules. We investigate whether rough-set-inspired methods can help in the steps of formulating feature subset optimization criteria, feature subset search heuristics, and the strategies of voting among classifiers. Comparing to our previous research, we pay a special attention to synchronization of the filter-based criteria for feature subset selection and extraction of rules basing on the obtained feature subsets. The overall framework is not supposed to produce the best-ever classification results, unless it is extended by some additional techniques known from the literature. Our major goal is to illustrate in a possibly simplistic way some general interactions between the above-mentioned criteria.


Feature Subset Selection Classifier Ensembles Rough Sets 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dominik Ślęzak
    • 1
    • 2
  • Sebastian Widz
    • 3
    • 4
  1. 1.Institute of MathematicsUniversity of WarsawWarsawPoland
  2. 2.Infobright Inc.WarsawPoland
  3. 3.Systems Research InstitutePolish Academy of SciencesWarsawPoland
  4. 4.XPLUS SAWarsawPoland

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