【作者】 夏叶娟；

【导师】 饶泓；

【作者基本信息】 南昌大学 ， 计算机软件与理论， 2008， 硕士

【摘要】 粗糙集理论是一种处理不准确、不确定和不完备信息的有效分析工具,能利用现有知识库中的知识对不完备信息进行近似刻画处理。属性约简和决策规则提取是粗糙集的两大核心研究内容,但现有的属性约简算法和决策规则提取方法都存在各种不足。为了获得更精简的属性约简集并有效提取决策规则,本论文首先针对基于分明矩阵的属性约简算法中构造分明函数时存在的元素重复、化简计算量大、矩阵元素长度不一等缺陷进行了改进。由于决策树技术具有分类速度快、效率高、容易理解等特点,本论文将其与粗糙集理论相结合实现决策规则的提取。利用上述改进的属性约简算法得到约简集,再利用约简集构造一棵具有多变量多集合的决策树,从而提取决策规则。为避免不一致信息的干扰,引入准确度和覆盖度两个评价因素对决策规则进行筛选,最后提取有效的决策规则。通过旋转机械中转子不对中的故障诊断实例对上述改进算法进行验证,实例表明,改进的属性约简算法比改进前的算法在故障规则提取时间上更快,证明了改进算法的有效性;同时也表明用粗糙集与决策树相结合的方法,不仅可以去除噪声,也可以处理不一致信息,最终能得到有效的故障诊断决策规则集。为了将上述方法更好应用到实践中,本论文在.NET平台上设计和实现了一个基于粗糙集的决策规则提取系统,此系统可对原始决策表进行属性约简、根据约简集构造出决策树进行规则提取、并引入覆盖度对规则进行筛选获得有效规则。更多还原

【Abstract】 Rough Set theory is a kind of effective analysis tool to deal with inaccurate, incomplete, and uncertain information, which makes use of the existed rules in the knowledge warehouse to character the incomplete information. Attribute reduction and decision rules extraction are two main respects research area of rough set, but there are many defects in the existing the two algorithms.In order to obtain a more streamlined set of attribute reduction effectively, the paper optimizes the attribute reduction algorithm that based on discernibility matrix firstly. Because there are some defects, such as element duplication, complex calculation, and varying length of discernibility matrix element in constructing the traditional discemibility function. As the decision tree technology is characteristic with fast classification speed, high efficient, easily to be understood, and so on, the paper combines the decision tree and rough set theory to extract decision rules, in which, the optimized attribute reduction algorithm is applied to get reduction sets and then the reduced attribute set is used to construct a multi-variable decision tree to extract decision rules. At last, in order to avoid disturbance of inconsistencies information, the accuracy and coverage degree are introduced to filter the decision rules and extract decision rules effectively. An example of rotating machinery fault diagnosis validated the above optimized algorithm, which shows the methods combines rough set and decision tree can not only wipe off noise, but also deal with inconsistencies information.In order to put the above optimized method into practice, a decision rule extraction system based on rough set theory and decision tree is developed in this paper. The system is designed based on .NET platform, which can carry out attribute reduction for original decision table, extract decision rules according to the structured decision tree, and obtain the effective decision rules finally.更多还原