Improved Decision Tree Algorithm Based on Samples Selection

To raise the accuracy of decision tree classification algorithms,an improved decision tree classification algorithm based on samples selection was proposed by comparing several classical decision tree classification algorithms.This improved algorithm searches better samples through a constantly iterative process based on the facts that the correlation between decision trees' accuracy and samples is large and decision trees can only get a local optimal solution.As a result,a better decision tree classification algorithm can be obtained under the condition of not changing the decision tree classification algorithm.The improved algorithm is not aiming at a decision tree and it carries through iteration only based on some feedback information of input and output,so its universality is better.Experimental results show that the ratio of the average error rates of the improved algorithm and the ID3,C4.5 algorithms is about 0.82 to 1.22 to 0.92.