Simplifying Decision Trees Learned by Genetic Programming

This work is motivated by financial forecasting using genetic programming. This paper presents a method to post-process decision trees. The processing procedure is based on the analysis and evaluation of the components of each tree, followed by pruning. The idea behind this approach is to identify and eliminate rules that cause misclassification. As a result we expect to keep and generate rules that enhance the classification. This method was tested on decision trees generated by a genetic program whose aim was to discover classification rules in financial stock markets. From experimental results we can conclude that our method is able to improve the accuracy and precision of the classification.