A Bi-Level Evolutionary Model Tree Induction Approach for Regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{mahouachi:2024:CEC,
-
author = "Safa Mahouachi and Maha Elarbi and Khaled Sethom and
Slim Bechikh and Carlos A. Coello Coello",
-
title = "A Bi-Level Evolutionary Model Tree Induction Approach
for Regression",
-
booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
-
year = "2024",
-
editor = "Bing Xue",
-
address = "Yokohama, Japan",
-
month = "30 " # jun # " - 5 " # jul,
-
publisher = "IEEE",
-
keywords = "genetic algorithms, genetic programming, Computational
modeling, Evolutionary computation, Machine learning,
Numerical models, Regression tree analysis, Genetic
operators, Convergence, Model Trees, Induction,
Regression, Bi-level optimization, Evolutionary
Algorithm",
-
isbn13 = "979-8-3503-0837-2",
-
DOI = "doi:10.1109/CEC60901.2024.10611959",
-
abstract = "Supervised machine learning techniques include
classification and regression. In regression, the
objective is to map a real-valued output to a set of
input features. The main challenge that existing
methods for regression encounter is how to maintain an
accuracy-simplicity balance. Since Regression Trees
(RTs) are simple to interpret, many existing works have
focused on proposing RT and Model Tree (MT) induction
algorithms. MTs are RTs with a linear function at the
leaf nodes rather than a numerical value are able to
describe the relationship between the inputs and the
output. Traditional RT induction algorithms are based
on a top-down strategy which often leads to a local
optimal solution. Other global approaches based on
Evolutionary Algorithms (EAs) have been proposed to
induce RTs but they can require an important
calculation time which may affect the convergence of
the algorithm to the solution. In this paper, we
introduce a novel approach called Bi-level Evolutionary
Model Tree Induction algorithm for regression, that we
call BEMTI, and which is able to induce an MT in a
bi-level design using an EA. The upper-level evolves a
set of MTs using genetic operators while the
lower-level optimises the Linear Models (LMs) at the
leaf nodes of each MT in order to fairly and precisely
compute their fitness and obtain the optimal MT. The
experimental study confirms the outperformance of our
BEMTI compared to six existing tree induction
algorithms on nineteen datasets.",
-
notes = "also known as \cite{10611959}
WCCI 2024",
- }
Genetic Programming entries for
Safa Mahouachi
Maha Elarbi
Khaled Sethom
Slim Bechikh
Carlos Artemio Coello Coello
Citations