Rough sets, especially fuzzy rough sets, are supposedly a powerful mathematical tool to
deal with uncertainty in data analysis. This theory has been applied to feature selection …

The aim of this paper is to introduce the notion of a rough soft hemiring, which is an
extended notion of a rough hemiring and a soft hemiring. We study roughness in soft …

This article will focus on the fourth V, the veracity, to demonstrate the essential impact of
modeling uncertainty on learning performance improvement. Low veracity corresponds to …

Abstract International Journal of Machine Learning and Cybernetics (IJMLC) is one of the
influential journals in the area of computer science, and it published its first issue in 2010 …

Cost sensitivity is an important problem, which has been addressed by many researchers
around the world. As far as cost sensitivity in the rough set theory is concerned, two types of …

Currently, most robust classifiers with parameters focus on the determination of the optimal
or suboptimal parameters. There are no research studies or even discussions about robust …

Generalized neighborhood system-based rough sets (GNSs) play a key part in rough set
theory. In this paper, we introduce a new approach to GNSs from the aspects of convex …

Rough sets provide an efficient tool for attribute reduction and rule extraction. However,
many important problems in rough set theory, including attribute reduction, are NP-hard and …

Ordinary topology now has been used in many sub-fields of artificial intelligence, such as
knowledge representation, spatial reasoning etc. In this paper, we discuss the relationships …

Rough sets are efficient for data pre-processing in data mining. However, some important
problems such as attribute reduction in rough sets are NP-hard, and the algorithms to solve …