A sampling strategy on decision tree for large data sets
Zhao Zhuang Guo · Microcomputer & its Applications · 2010
To raise the accuracy of decision trees on extensive data sets,proposed a new kind of way to sample on data sets.Pre-generated a decision tree using some fast decision tree algorithms,divide the decision tree into some data sets in predefined limit by BSF manner,then sample on every set in random,integrate all sets into target set.Experiment on an UCI data set show that the ratio of average correct rates is higher than traditional random sample.