Research On Rule Rule Acquisition In Incomplete And Fuzzy Objective Information Systems

Classical Pawlak rough set theory is one of important tools for data analyzing and processing in complete information system. The main idea is to deduce the classification rules or decision-making rules by knowledge reduction on the conditions of the same capability of classification. In fact, the complete information system is only an ideal situation. Because conditional attributes may be incomplete, objective attributes may appear ambiguous situations, which is called the rough set model of incomplete and fuzzy objectives information system (It is called IFOIS for short) in this paper. So, it has very important practical significance to research IFOIS.In this paper, addressing IFOIS, we research the knowledge acquisition approach based on rough set theory and fuzzy set theory. The expanded rough set models of IFOIS are established under the condition of improved-tolerance relation, limited-tolerance relation and symmetric similarity relation. Furthermore, we discussed all kinds of knowledge reduction approaches among different models and proposed three advanced algorithms, which include precision reduction algorithm which is (α,β) binary relation under improve-tolerance relation,β-upper precision reduced (orα-lower precision reduced) under limited-tolerance relation and (α,β) coordinated reduction resolution matrix algorithm under symmetric similarity relation. At last, through analysising extraction of the decision-making rules of IFOIS for four different binary relations, we proposed the corresponding rule extraction algorithms and proved their feasibility by some examples .