Attribute Reduction in Continuous Decision System
|Course||Operational Research and Cybernetics|
|Keywords||real number rough set attributes reduction broad sense attribute significance compositor interval number sort decision matrix decisiom rule|
Rough set model (RSM) is a new mathematical approach to uncertain and vague data analysis, which is proposed by mathematician Pawlak. The application of rough set theory for machine learning, knowledge acquisition, decision analysis, expert system and pattern recognition has been proved to be very successful.But with the development of RSM, the limitation of RST is exposed gradually. The classification of object must be correct and positive. This led to some useful information were lost during the process of rule extraction which limits the applications of RSM. In recent years, a lot of scholars extend RST from many aspects, such as VPRS model, probabilistic rough sets model, generalized rough sets model and fuzzy rough sets model and so on.The real rough set (RRS) model was proposed to deal with consecutive attribute decision-making table. It introduces the error rate and gets rid of the step of scattering data based on generalized degree of magnitude. It reduces the kickback from decreasing information in data processing, and boosts up the ability of resisting interference and forecasting new data.In this paper, we mainly studies attribution reduction of the information system based on the RRS model. There are six chapters in this paper. In the first chapter,it mainly introduces the situation of Pawlak rough set theory and it’s mostly aspects of investigation. In the second chapter, the basic conception of Pawlak rough set model, the information system, attribute reduction. In the third chapter, broad sense attribute significance and RRS model and attribute reductions of RRS model are introduced. At the same time the methods of them were given. A practical example is given to explain the validity and feasibility of algorithms.In the forth chapter, interval numbers attributer decision making is introduced, and attribute reductions are introduced in two methods.A practical example is given to explain the validity and feasibility of algorithms. In the fifth chapter, a system of attributes reduction based on RRS is empoldered. The system can automatic give the reduction of attributes and compositor interval numbers when data was imported. In the sixth chapter, we study rule induction from two decision tables as abasis of rough set analysis of more than on decision.We point out that the set of all decision rules from two decision tables can be splited in two levels:a first level decision rule is positively supported by a decision table and does not have any conflict with the other decision table and a second level decision rule is positively supported by decision tables.To each level, we propose rule induction methods based on decision matrices.Through the discussions, we demonstrate that many kinds of rule inductions are conceivable.In the last part, the summarization of this paper and the expectation of scientific research in the futher are given.