Research On Relative-Attribute Reduction Algorithm And Decision-Making Method Based On Rough Set

Rough set is an excellent data analysis tool to process inconsistent, inaccurate, incomplete information and so on. Its theories and methods have been applied to many areas successfully including pattern recognition, machine learning, decision support, knowledge discovery, and forecast modeling and so on. The relative-attribute reduction algorithm and decision-making method are key technologies of rough set theory and application and becoming a research focus of concern.Surrounding the four key problems of relative-attribute reduction and decision-making method, i.e. relative-attribute reduction, rules of acquisition, decision method based on rough set and its prototype system, the following six works have been done: greedy relative-attribute reduction algorithm, top-down pruning relative-attribute reduction algorithm based on core, greedy decision rules of acquisition, support vector decision-making methods based on rough set, optimal support vector decision-making methods based on rough set, and decision-making model of support vector ensembles based on rough set.For the problem of questing single relative-attribute reduct, the greedy relative-attribute reduction algorithm, which takes classification ability of condition attributes for heuristic information, can be an effective algorithm to achieve more intuitive to solve the above. When the decision-making information system contains a large number of objects, the algorithm can save a lot of storage space and is suitable for large-scale data sets on the calculation. It tends to choose the condition attributes of higher classification ability added to the relative attribute reduct. According reduction goal, this tendency is reasonable.When a decision-making information system consists of a considerable number of attributes and a large number of records, how to get the relative-attribute reduct including least condition attributes or the whole set of all relative-attribute reducts from the decision-making information systems, is a worthy subject of research. Top-down pruning relative-attribute reduction algorithm based on core is a viable solution to the problem. Experimental results show that top-down pruning relative-attribute reduction algorithm based on core is feasible and effective.According to different measure of decision-making rules from different aspects, the greedy methods obtaining decision-making rules of consistency or inconsistency are proposed. The methods, which take classification ability of condition attributes for heuristic knowledge to guide the attribute value reduction process, not only get the shorter rules of the strong performance in the classification of forecasts, but also improve the speed and save the storage space.By following the increasing of the amount of data, the weakness of fault tolerance and generalization capability is to the fore in rough set theory. Therefore, it is a worthy subject of study how to improve fault-tolerant ability and generalization ability of the decision-making. Three kinds of support vector decision-making methods based on rough sets are proposed from different aspects. By comparing the other methods, experimental results show that our proposed algorithms have higher fault-tolerant ability and generalization ability.Based on the above research findings, as well as the related technology, designed and implemented a prototype system based on relative-attribute reduction algorithms and decision-making methods. Compared with similar systems, this prototype system is designed to achieve that has certain uniqueness, with higher quality results of knowledge discovery and decision-making, and has good fault-tolerant ability and generalization ability, but also has strong robustness.