Criteria for choosing a rough set model

  • Authors:

  • Joseph P. Herbert;JingTao Yao

  • Affiliations:

  • Department of Computer Science, University of Regina, Regina, Saskatchewan, S4S 0A2, Canada;Department of Computer Science, University of Regina, Regina, Saskatchewan, S4S 0A2, Canada

  • Venue:
  • Computers & Mathematics with Applications
  • Year:
  • 2009

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Abstract

One of the challenges a decision maker faces in using rough sets is to choose a suitable rough set model for data analysis. We investigate how two rough set models, the Pawlak model and the probabilistic model, influence the decision goals of a user. Two approaches use probabilities to define regions in the probabilistic model. These approaches use either user-defined parameters or derive the probability thresholds from the cost associated with making a classification. By determining the implications of the results obtained from these models and approaches, we observe that the availability of information regarding the analysis data is crucial for selecting a suitable rough set approach. We present a list of decision types corresponding to the available information and user needs. These results may help a user match their decision requirements and expectations to the model which fulfills these needs.