样本信息处理中一种属性约简方法的研究

Study on Attribute Reduction Method in Sample Information Processing

为了剔除样本信息中存在的冗余成分和不相容性,同时提取关键信息等,根据样本信息的特点和信息具有粒度的思想,基于粗糙集的2个近似精度科学地定义了条件属性重要性,进而提出一种对样本信息进行属性约简的有效、简便方法.该方法主要包括信息核的求取、可省条件属性的重要性计算和相对属性约简集的确定.其中,为连续属性的离散化处理提供了一种基于模糊相似比原理的快速离散化算法,它能起到剔除模糊噪声的作用.典型实例计算和在油水层识别系统中的实际应用表明,这种属性约简方法的识别准确率可达90%以上,应用效果显著.

In order to eliminate redundancies and incompatibilities in samples, extract the key information, and so on, the condition attribute significance was defined scientifically based on two approximation qualities of rough set according to the characteristics of sample information and the thought of information granularity. Moreover, a simple effective method of attribute reduction was presented, the main process was to determine the information core, calculate significance of dispensable condition attribute and obtain the relative attribute reduction set. For the continuous attribute processing, a speedy discretization algorithm was presented based on fuzzy similar-ratio principle which can eliminate the fuzzy noise. The typical example calculation and the actual application in oil-water formation recognition system show that discernment accuracy of the attribute reduction method can approach more than 90%, and the application effect is very notable.