How Good Are Probabilistic Approximations for Rule Induction from Data with Missing Attribute Values?

  • Patrick G. Clark
  • Jerzy W. Grzymala-Busse
  • Zdzislaw S. Hippe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7413)

Abstract

The main objective of our research was to test whether the probabilistic approximations should be used in rule induction from incomplete data. Probabilistic approximations, well known for many years, are used in variable precision rough set models and similar approaches to uncertainty.

For our experiments we used five standard data sets. Three data sets were incomplete to begin with and two data sets had missing attribute values that were randomly inserted. We used two interpretations of missing attribute values: lost values and “do not care” conditions. Among these ten combinations of a data set and a type of missing attribute values, in one combination the error rate (the result of ten-fold cross validation) was smaller than for ordinary approximations; for other two combinations, the error rate was larger than for ordinary approximations.

Keywords

Image Segmentation Probabilistic Approximation Decision Table Rule Induction Indiscernibility Relation 
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-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Patrick G. Clark
    • 1
  • Jerzy W. Grzymala-Busse
    • 1
    • 2
  • Zdzislaw S. Hippe
    • 3
  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of KansasLawrenceUSA
  2. 2.Institute of Computer SciencePolish Academy of SciencesWarsawPoland
  3. 3.Department of Expert Systems and Artificial IntelligenceUniversity of Information Technology and ManagementRzeszowPoland

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