An Improved Algorithm for CART Based on the Rough Set Theory

Abstract

Data prediction and classification is a critical method in medical nutrition data analysis area. As for the characteristics of being intuitive, efficient and easy to understand, the decision tree algorithm is widely used in this field. However, the classification rules extracted from the decision tree are not the most simple and efficient. The paper analyzes the classical decision tree algorithm CART, and proposes a new improved algorithm R2-CART. The core idea of the advanced algorithm is, in order to simplify the classification rules and tree, combining CART algorithm with rough set theory to conduct the attribute and rule reduction on the classification rules of decision tree. The experiment, which compares the Original CART algorithm with the improved algorithm, shows that the improved algorithm has much better classification efficiency with achieving a simple and efficient classification rule set at the same time. This improved algorithm has a potential practical value for large-scale medical nutrition data of classification and predictive analysis.