基于WEKA平台的决策树算法比较研究

Comparative Study on Decision Tree Algorithm Based on WEKA Platform

决策树是数据挖掘领域广泛研究和应用的一种分类算法,具备计算量小、速度快、分类准确率高、分类规则易于理解等众多优点。论文选取了八个公开的UCI科研数据集,从分类准确率、建模速度、可解释性三个方面对经典的决策树算法C4.5、CART和NBTree进行比较,分析了三个算法各自的原理和优缺点,明确了各算法的适用情况。

Decision tree is a kind of classification algorithm which is widely researched and applied in data mining. It hasmany advantages such as small computation,fast speed,high classification accuracy and easy to understand classification rules.This paper selects eight open UCI scientific data sets,and compares the classic decision tree algorithm C4.5,CART and NBTreefrom three aspects：classification accuracy,modeling speed,and interpretability. In this paper,the principles and advantages anddisadvantages of the three algorithms are analyzed,and the application of each algorithm is clarified.