Performance Analysis between Different Decision Trees for Uncertain Data
Xiaoming Peng, Haoran Guo, Jianmin Pang · 2012
In order to compare the classification accuracies and performance differences between traditional and probability-based decision tree classifiers, and come to understand those algorithms, which aim to improve construction efficiency of probability-based decision trees, mentioned in "Decisions Trees for Uncertain Data", this paper tested several algorithms, named AVG, UDT, UDT-BP, UDT-LP, UDT-GP, and UDT-ES respectively which based on the source codes of UDT program version 0.9. Extensive experiments have been conducted and the results show that: (1) Probability-based classifiers are more accurate than those using value averages. (2) Comparing with other pruning algorithms, UDTES algorithm performs the best when pruning probability-based decision trees.