粗决策树动态规则提取算法研究及应用  

Research and application of dynamic rule extraction algorithm based on rough set and decision tree

作　　者：陈丽芳 王云 张奉 

机构地区：[1]华北理工大学理学院,河北唐山063009

出　　处：《计算机应用》2015年第11期3222-3226,共5页journal of Computer Applications

基　　金：河北省自然科学基金资助项目(F2014209086)

摘　　要：针对静态算法对大数据和增量数据处理不足的问题,构造了基于粗决策树的动态规则提取算法,并将其应用于旋转机械故障诊断中。将粗集与决策树结合,用增量方式实现样本抽取;经过动态约简、决策树构造、规则提取与选择、匹配4个步骤的循环迭代过程,实现了数据的动态规则提取,使得提取的规则具有更高的可信度;同时,将算法应用于旋转机械故障诊断这一动态问题中,验证了算法的有效性;最后,将所提算法分别与静态算法和增量式动态算法进行了效率对比分析,实验结果表明,所提算法能够以最精简的规则获得更多数据隐含信息。For the shortage of big data and incremental data processing in static algorithm, the dynamic rule extraction algorithm based on rough-decision tree was constructed to diagnose rotating machinery faults. Through the combination of rough set with decision tree, the sample selections were made by the method of incremental sampling. Through dynamic reduction, decision tree construction, rules extraction and selection, matching, four steps of loop iteration process, dynamic rule extraction was achieved, which improved the credibility of the extracted rules. Meanwhile, by applying the algorithm to the dynamic problem： rotating machinery fault diagnosis, the effectiveness of the algorithm was verified. Finally, the efficiency of the algorithm was compared with static algorithm and incremental dynamic algorithm. The result demonstrates that the proposed algorithm can obtain more implied information in the most streamlined way.

关 键 词：粗集 决策树 静态算法 动态约简 动态规则 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构] O29[自动化与计算机技术—计算机科学与技术]