Fuzzy rough sets: Survey and proposal of an enhanced knowledge representation model based on automatic noisy sample detection

Fuzzy Rough Set (FRS) theory, which has been emerged thanks to unifying Rough Set and Fuzzy Set ones, is a powerful mathematical tool for handling and processing real data of imprecise, incomplete, inconsistent and uncertain nature. It has drawn attention of many researchers, scientists and industrials in various domains over the last three decades. However, different studies have showed that its classical knowledge representation model has a main weakness linked to its sensitivity to data noise which decreases both its effectiveness and application scope. In this paper, we survey the current FRS paradigms developed to deal with this issue and propose a new FRS model based on the Automatic Noisy Sample Detection (ANSD-FRS) able to cope with noise influence in classification tasks. Besides, we study the principal properties of this new model and reformulate the most applied FRS concepts relying on its operators. Numerous experiments have been conducted to analyze the ANSD-FRS behavior compared to the commonly used FRS models reputed as the most noise-resistant paradigms. These experiment results have proved the performance and robustness of the ANSD-FRS in comparison with those renowned models.