Rough set model based on fuzzy measure

Rough sets theory (RS) is a new tool for processing fuzzy and uncertain knowledge, and has already been applied to many areas successfully. By analyzing the basic characteristics of rough set, we find that the existing rough set model can´t effectively solve data reduction and knowledge discovery with fuzzy feature. In order to improve it, in this paper, we first propose rough sets model based on fuzzy measure (denoted by BF-RS for short), then we analyze the effectiveness of the model through an example. The result indicates BF-RS can not only have the advantages of classical rough set, but also it can solve effectively information processing problem with the fuzzy characteristics, and can be widely used in many fields such as data mining, evidence theory, artificial intelligence and so on.