Research and application of dynamic rule extraction algorithm based on rough set and decision tree
Lifan Chen · Journal of Computer Applications · 2015
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.