Knowledge acquisition in incomplete information systems based on variable precision rough set model
Wei-Zhi Wu · 2005
This paper deals with knowledge acquisition in incomplete information systems using variable rough set model. We introduce the concepts of /spl beta/-lower and /spl beta/-upper approximations. We also propose reduction of knowledge that eliminates only that information, which is not essential from the point of view of classification or decision making within /spl beta/ precision. In our approach we make only one assumption about unknown values: the real value of a missing attribute is one from the attribute domain. We show how to find decision rules directly from such an incomplete decision table, which are as little non-deterministic as possible and have minimal number of conditions.