Comparative Studies of Covering Rough Set Models

Generalized Rough Set Models

A comprehensive study of fuzzy covering-based rough set models: Definitions, properties and interrelationships

Fuzzy covering-based rough set models are hybrid models using both rough set and fuzzy set theory. The former is often used to deal with uncertain and incomplete information, while the latter is used to describe vague concepts. The study of fuzzy rough set models has provided very good tools for machine learning algorithms such as feature and instance selection. In this article, we discuss diff...

Decision-Theoretic Rough Set Models

Decision-theoretic rough set models are a probabilistic extension of the algebraic rough set model. The required parameters for defining probabilistic lower and upper approximations are calculated based on more familiar notions of costs (risks) through the well-known Bayesian decision procedure. We review and revisit the decision-theoretic models and present new results. It is shown that we nee...

Diagnostic Reasoning with Multilevel Set-Covering Models

We consider multilevel set-covering models for diagnostic reasoning: though a lot of work has been done in this field, knowledge acquisition efforts have been investigated only insufficiently. We will show how set-covering models can be build incrementally and how they can be refined by knowledge enhancements or representational extensions. All these extensions have a primary characteristic: th...

1 a Review of Rough Set Models

Since introduction of the theory of rough set in early eighties, considerable work has been done on the development and application of this new theory. The paper provides a review of the Pawlak rough set model and its extensions, with emphasis on the formulation, characterization, and interpretation of various rough set models.