A CART-based genetic algorithm for constructing higher accuracy decision trees

Abstract

Decision trees are among the most popular classification methods due to ease of implementation and simple interpretation. In traditional methods like CART (classification and regression tree), ID4, C4.5; trees are constructed by myopic, greedy top-down induction strategy. In this strategy, the possible impact of future splits in the tree is not considered while determining each split in the tree. Therefore, the generated tree cannot be the optimal solution for the classification problem. In this paper, to improve the accuracy of the decision trees, we propose a genetic algorithm with a genuine chromosome structure. We also address the selection of the initial population by considering a blend of randomly generated solutions and solutions from traditional, greedy tree generation algorithms which is constructed for reduced problem instances. The performance of the proposed genetic algorithm is tested using different datasets, varying bounds on the depth of the resulting trees and using different initial population blends within the mentioned varieties. Results reveal that the performance of the proposed genetic algorithm is superior to that of CART in almost all datasets used in the analysis.