Abstract
With improvement of the level of modernization and mechanization of railway infrastructure maintenance, Railway large maintenance machinery (RLMM) is considered to be one of the main equipment. We have accumulated large amounts of RLMM construction data. How to mine valuable information from these data has become an important research subject. The C4.5 algorithm of decision tree is a useful method of data mining and classification. The paper solves evaluation of RLMM construction problem based on the C4.5 algorithm. By means of extracting the attribute with maximum gain ratio as the root node of the decision tree from training sample data, the evaluation decision tree was constructed. The decision tree modeling of evaluation of RLMM construction was gained by the post-pruning approach. The experimental results and analysis show that this model has high precision and credibility.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
