Open AccessApplication of genetic programming algorithm for designing decision trees and their ensembles
Author(s) -
S. A. Mitrofanov
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/734/1/012098
Subject(s) - decision tree , genetic programming , incremental decision tree , id3 algorithm , computer science , interpretability , decision tree learning , genetic algorithm , boosting (machine learning) , machine learning , decision stump , cultural algorithm , algorithm , artificial intelligence , alternating decision tree , population based incremental learning
Decision tree is a machine learning algorithm that is very effective in classification problems. Decision tree is a topical algorithm because of the good interpretability of the results of their work. However, decision tree has the drawback that standard algorithms don’t allow obtaining the optimal structure of the decision tree. To solve this drawback, it’s proposed to use the genetic programming algorithm. This algorithm is one of the branches of evolutionary algorithms and has proven itself for the design of intelligent information technologies. Genetic programming, in one of their implementations, searches for solutions in tree space, which is well suited for designing decision trees. Ensembles that are based on decision trees have high efficiency. In this paper, random forest and gradient boosting are considered. In ensembles, it’s proposed to combine decision trees that are designed by the genetic programming algorithm. Algorithms was tested on classification problems.