【作者】 李泓波；

【导师】 张健沛；

【作者基本信息】 哈尔滨工程大学 ， 计算机软件与理论， 2008， 硕士

【摘要】 数据融合技术已经成为当今信息产业的亮点和热点,其应用已经涵盖了军事和民事等诸多领域。由于多种原因都会造成数据在传输过程的遗失,而最小属性约简问题已被证明是NP难题,所以寻求高效、实用的数据补齐算法和属性约简算法是众多研究者一直致力解决的问题。基于粗糙集理论在处理不确定问题时较之模糊集、D-S证据理论等其他工具的独特优势,本文采用粗糙集理论研究决策级数据融合的数据补齐和属性约简问题。首先,本文深入分析了一个经典数据补齐算法及其改进算法,发现了该算法及其改进算法存在数据补齐资格不可控、适用范围受限等缺陷。通过引入相容阈值和标准化距离函数,本文提出了一个基于量化容差关系的改进算法。新算法改进了数据之间相似性的刻画手段,避免了数据之间相似性的片面刻画,降低了时空开销,实现了补齐资格的可控性,扩大了原算法的适用范围。其次,通过对一个属性约简算法的分析验证,本文发现了该算法存在着在核基为零条件下无法进行有效约简的缺陷,进而剖析了该缺陷存在的根源,指出了该缺陷存在的普遍性,并针对这个缺陷提出了真核、伪核的概念和一系列相关定理,然后基于这些概念和定理给出了改进算法。新算法克服了原算法的缺陷,提高了适用性。决策级数据融合的数据补齐和属性约简算法直接关系到最终决策质量,本文提出的两个改进算法可望比原算法有更好的表现。更多还原

【Abstract】 Nowadays data fusion technology has become a bright spot or a hot spot in IT and its application includes many fields, such as military and civil ones. For data will be missing by all kinds of reasons in the transiting procedure and minimum attribute reduction problem has been proved a NP-hard one, finding a effective and practical data complement and attribute reduction algorithm always are tempting puzzles to many researchers. Though we have some comparatively mellow algorithms to resolve the two problems, they all have some flaws. Because of the rough set theory have some particular advantages than other tools, such as vagueness set and D-S evidence theory, so this thesis selects it to study the decision level’s data fusion.Firstly, this thesis deeply analyzes a classical data complement algorithm and it’s improved algorithms, finds many flaws of them, which includes the competency of the filling data can’t be controlled, the application scope of the algorithms is restricted, etc. This thesis, based on valued tolerance relation, presents an improved algorithm by introducing a tolerance threshold and a normalized distance function to give a further improvement. The new algorithm improves the depicting approach of two piece of data’s comparability, and avoids the unilateral description of two piece of data’s comparability. It reduces the cost of time and space computation, realizes the control ability of filling qualification, and enlarges the application scope of the old algorithms. Secondly, by validating and analyzing, this thesis finds a flaw in an attribute reduction algorithm, which cause the algorithm can’t effectively deal with the attribute reduction when the cardinal number of core equals zero. This thesis not only analyzes the source of the flaw but also points out that the flaw widely exists in many attribute reduction algorithm. Aimming at the flaw this thesis introduces hypo-core and real-core concepts and a series of their concerning theorems. Based on these concepts and theorems this thesis improved the attribute reduction algorithm. The new algorithm amends the flaw in the original, enlarges the application scope.For the quality of decision rules depends on the data complement and attribute reduction algorithms of the decision level of data fusion, the two improved algorithms hopely have better performances than the the originals.更多还原