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.
出处
《计算机应用》
CSCD
北大核心
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)
关键词
粗集
决策树
静态算法
动态约简
动态规则
rough set
decision tree
static algorithm
dynamic reduction
dynamic rule
