2025

Journal

Neurocomputing, 646, 130280

Multi-output Regression (MOR) is a machine learning technique that aims to predict several values simultaneously. Some existing approaches addressed this problem by decomposing the MOR problem into separate single-target ones. However, in real-world applications, it is more advantageous to exploit the inter-target correlations in the prediction task. Some other approaches proposed simultaneous prediction but they are based on greedy algorithms and are prone to fall easily into local optima. In order to solve these issues, we propose a novel approach called Bi-level Evolutionary Model TreeChain Induction (BEMTCI) which is able to deal with multi-output datasets using a bi-level evolutionary algorithm. BEMTCI evolves a population of Model Tree Chains (MTCs) where each Model Tree (MT) focuses on the prediction of one single target. The upper-level explores different orderings of the MTs of each MTC to find the best chaining order which is able to express the relationships among the output variables. A further optimization is performed in the lower-level of BEMTCI which concerns the linear models at the leaves of the MTs. The experimental study showed the effectiveness of our approach compared to the existing ones when applied on sixteen MOR datasets. The genetic operators employed in our BEMTCI ensure the variation of the population and guarantee a fair and a precise prediction due to the evaluation process. The obtained results prove the performance of our BEMTCI in solving MOR problems.

@article{MAHOUACHI2025130280,
title = {Bi-level Evolutionary Model Tree Chain Induction for Multi-output Regression},
journal = {Neurocomputing},
volume = {646},
pages = {130280},
year = {2025},
issn = {0925-2312},
doi = {https://doi.org/10.1016/j.neucom.2025.130280},
url = {https://www.sciencedirect.com/science/article/pii/S092523122500952X},
author = {Safa Mahouachi and Maha Elarbi and Slim Bechikh},
keywords = {Multi-output regression, Outputs’ correlation, Model tree chain induction, Bi-level optimization, Evolutionary algorithm},
}