Decision Tree Algorithm by Gene Expression Programming Based on Differential Evolution

Uniformly distributed constants-based decision tree evolved by Gene Expression Programming(GEP) is a kind of classifier with fairly high accuracy,but its performance on multi-attribute data classification is not satisfactory.This paper presents an algorithm of Differential Evolution(DE)-based decision tree algorithm by GEP.This new algorithm uses differential evolution method to improve the additional threshold,and makes the uniform constant array have both uniformly and diversity.Experiments on benchmark datasets show it performs better on multi-attribute classification problems than basic GEP decision tree.