Based On Attribute Reduction Of Rough Set Theory, Decision Table And Rules Reduction Algorithm Research And Related Applications

Rough set theory , proposed in early 1982, is a new mathematical tool in dealing with imprecision and uncertainty. The most remarkable characteristic of this theory is that it does not require any additional empirical information of data sets. By analysising and reasoning the database, this theory can discover the patterns and rules hidden in database. In recent years, rough set theory has been applied to many areas successfully including pattern recognition, machine learning, decision support, predictive modeling, also it has entensive application in the military field.Many researchers pay attention to the study of the algorithm using rough set approach, such as generation of decision rule algorithm, reduction of attributes algorithm, the parallelism algorithm and the algorithm associated the neural networks or GA with rough set and so on. The attributes reduction and decision rule generation are the kernel contents of rough set theory.In this thesis, we study deeply the methods of decision rule generation and attributes reduction, Some available methods are summarized. At the same time , a heuristic attribute reduction method based on discernibility matrix is proposed firstly. Then, it is presented a method of attributes reduction associated GA with rough set, which is also applied in incomplete information system. As for the method of decision rule generation, at the first we propose an effective algorithm of finding the core of attributes value. Then, a modified algorithm for acquisition of decision rules based on classification consistency rate is presented. At the end of this paper, rough set theory is applied in the military field of comprehensive evaluation.