✦ LIBER ✦

Criteria for choosing a rough set model

✍ Scribed by Joseph P. Herbert; JingTao Yao

Publisher: Elsevier Science
Year: 2009
Tongue: English
Weight: 576 KB
Volume: 57
Category: Article
ISSN: 0898-1221
DOI: 10.1016/j.camwa.2008.10.043

No coin nor oath required. For personal study only.

✦ Synopsis

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.

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