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Experiments on Data with Three Interpretations of Missing Attribute Values—A Rough Set Approach

  • Jerzy W. Grzymała-Busse
  • Steven Santoso
Part of the Advances in Soft Computing book series (AINSC, volume 35)

Abstract

In this paper we distinguish three different types of missing attribute values: lost values (e.g., erased values), “do not care” conditions (attribute values that were irrelevant for classification a case), and attribute-concept values (“do not care” conditions restricted to a specific concept). As it is known, subset and concept approximations should be used for knowledge acquisition from incomplete data sets. We report results of experiments on seven well-known incomplete data sets using nine strategies: interpreting missing attribute values in three different ways and using both lower and upper, subset and concept approximations (note that subset lower approximations are identical with concept lower approximations). Additionally, in the data sets cases with more than approximately 70% of missing attribute values, these values were removed from the original data sets and then all nine strategies were applied. Our conclusions are that any two of our nine strategies are incomparable in terms of error rates (5% significance level, two-tailed test). However, for some data sets removing cases with an excessive number of missing attribute values improves the error rate.

Keywords

Rule Induction Granular Computing Indiscernibility Relation Rule Induction Algorithm Incomplete Information System 
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 2006

Authors and Affiliations

  • Jerzy W. Grzymała-Busse
    • 1
    • 2
  • Steven Santoso
    • 1
  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of KansasLawrenceUSA
  2. 2.Institute of Computer SciencePolish Academy of SciencesWarsawPoland

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