MOGA: Multi-Objective Genetic Algorithm to select Stacking Ensemble Learning for classification

Stacking Ensemble Learning (SEL) has been effectively integrated with Multi-Objective Optimisation (MOO) heuristics for classification tasks across various domains, including finance, healthcare, and cybersecurity. This study aims to address the challenge of generalising SEL to a diverse set of classification cases. Thus, the Multi-Objective Genetic Algorithm (MOGA) framework is proposed, utilising a Genetic Algorithm (GA) to evolve a population of distinct SELs, each built from a varied subset of base models. The goal is to select the subset that composes the most effective SEL for a given classification task. MOGA is designed with two main objectives—maximising precision and recall—which helps to maintain independence from any specific classification case. In addition, incorporating models of varied types ensures adaptability and high performance in different situations. Comprehensive experimentation was conducted on 23 diverse datasets, where MOGA demonstrated high performance in nearly all datasets, outperforming other ensemble learning (EL) methods in 100% of the datasets in precision, 78% in recall, 69.5% in f1−score, and 78% in accuracy. A t-test analysis yielded results of p-value <0.05, indicating a statistically significant improvement in the accuracy of the MOGA over the base models. Moreover, the framework's application can be extended to regression tasks as well.