Evolutionary algorithms for constructing an ensemble of decision trees

02/03/2020
by   Evgeny Dolotov, et al.
0

Most decision tree induction algorithms are based on a greedy top-down recursive partitioning strategy for tree growth. In this paper, we propose several methods for induction of decision trees and their ensembles based on evolutionary algorithms. The main difference of our approach is using real-valued vector representation of decision tree that allows to use a large number of different optimization algorithms, as well as optimize the whole tree or ensemble for avoiding local optima. Differential evolution and evolution strategies were chosen as optimization algorithms, as they have good results in reinforcement learning problems. We test the predictive performance of this methods using several public UCI data sets, and the proposed methods show better quality than classical methods.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/17/2016

GENESIM: genetic extraction of a single, interpretable model

Models obtained by decision tree induction techniques excel in being int...
research
10/18/2022

Generalization Properties of Decision Trees on Real-valued and Categorical Features

We revisit binary decision trees from the perspective of partitions of t...
research
10/14/2020

Decision trees as partitioning machines to characterize their generalization properties

Decision trees are popular machine learning models that are simple to bu...
research
12/19/2017

A Faster Drop-in Implementation for Leaf-wise Exact Greedy Induction of Decision Tree Using Pre-sorted Deque

This short article presents a new implementation for decision trees. By ...
research
12/14/2020

Evolutionary learning of interpretable decision trees

Reinforcement learning techniques achieved human-level performance in se...
research
08/04/2022

Evolutionary bagging for ensemble learning

Ensemble learning has gained success in machine learning with major adva...

Please sign up or login with your details

Forgot password? Click here to reset