Learning rules from crisp attributes by rough sets on the fuzzy class sets

Machine learning can extract desired knowledge and ease the development bottleneck in building expert systems. Among the proposed approaches, deriving classification rules from training examples is the most common. Given a set of examples, a learning program tries to induce rules that describe each class. The rough-set theory has served as a good mathematical tool for dealing with data classification problems. In the past, the rough-set theory was widely used in dealing with data classification problems, that data sets were containing crisp attributes and crisp class sets. This paper thus extends rough-set theory previous approach to deal with the problem of producing a set of certain and possible rules from crisp attributes by rough sets on the fuzzy class sets. The proposed approach combines the rough-set theory and the fuzzy class sets theory to learn. The examples and the approximations then interact on each other to drive certain and possible rules. The rules derived can then serve as knowledge concerning the data sets on the fuzzy class sets.