Complexity of Rule Sets Induced by Two Versions of the MLEM2 Rule Induction Algorithm

  • Patrick G. Clark
  • Cheng Gao
  • Jerzy W. Grzymala-Busse
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10246)


We compare two versions of the MLEM2 rule induction algorithm in terms of complexity of rule sets, measured by the number of rules and total number of conditions. All data sets used for our experiments are incomplete, with many missing attribute values, interpreted as lost values, attribute-concept values and “do not care” conditions. In our previous research we compared the same two versions of MLEM2, called true and emulated, with regard to an error rate computed by ten-fold cross validation. Our conclusion was that the two versions of MLEM2 do not differ much, and there exists some evidence that lost values are the best. In this research our main objective is to compare both versions of MLEM2 in terms of complexity of rule sets. The smaller rule sets the better. Our conclusion is again that both versions do not differ much. Our secondary objective is to compare three interpretations of missing attribute values. From the complexity point of view, lost values are the worst.


Incomplete data Lost values Attribute-concept values “Do not care” conditions MLEM2 rule induction algorithm Probabilistic approximations 


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© Springer International Publishing AG 2017

Authors and Affiliations

  • Patrick G. Clark
    • 1
  • Cheng Gao
    • 1
  • Jerzy W. Grzymala-Busse
    • 1
    • 2
  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of KansasLawrenceUSA
  2. 2.Department of Expert Systems and Artificial IntelligenceUniversity of Information Technology and ManagementRzeszowPoland

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