Rough Classifiers Sensitive to Costs Varying from Object to Object

  • Andrzej Lenarcik
  • Zdzisław Piasta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1424)


We present modification of the ProbRough algorithm for inducing decision rules from data. The generated rough classifiers are now sensitive to costs varying from object to object in the training data. The individual costs are represented by new cost attributes defined for every single decision. In this approach the decision attribute is dispensable. Grouping of objects and defining prior probabilities are made on the basis of the group attribute. Values of this attribute may have no relations with the decisions. The proposed approach is a generalization of the methodology incorporating the cost matrix. Behavior of the algorithm is illustrated on the data concerning the credit evaluation task.


Learning Object Average Cost Group Attribute Cost Matrix Cost Criterion 
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 1998

Authors and Affiliations

  • Andrzej Lenarcik
    • 1
  • Zdzisław Piasta
    • 1
  1. 1.Mathematics DepartmentKielce University of TechnologyKielcePoland

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