Improving the β-precision and OWA based fuzzy rough set models: definitions, properties and robustness analysis

Since the early 1990s, many authors have studied fuzzy rough set models and their application in machine learning and data reduction. In this work, we adjust the beta-precision and the ordered weighted average based fuzzy rough set models in such a way that the number of theoretical properties increases. Furthermore, we evaluate the robustness of the new models a-beta-PREC and a-OWA to noisy data and compare them to a general implicator-conjunctor-based fuzzy rough set model

Rough Matroids Based on Dual Approximation Operators

This paper presents the concept of lower and upper rough matroids based on approximation operators for covering-based rough sets. This concept is a generalization of lower and upper rough matroids based on coverings. A new definition of lower and upper definable sets related with an approximation operator is presented and these definable sets are used for defining rough matroids based on an approximation operator. Finally, an order relation for a special type of rough matroids is established from the order relation among approximation operators