Ensemble classifiers using multi-objective Genetic Programming for unbalanced data

• Published in: Applied soft computing Vol. 158; p. 111554
• Main Authors: Meng, Wenyang, Li, Ying, Gao, Xiaoying, Ma, Jianbin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2024
• Subjects: Ensemble; Genetic Programming; Multi-objective; Unbalanced; Multi-objective; Genetic Programming; Unbalanced; Ensemble
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2024.111554

Genetic Programming (GP) can be used to design effective classifiers due to its built-in feature selection and feature construction characteristics. Unbalanced data distributions affect the classification performance of GP classifiers. Some fitness functions have been proposed to solve the class imbalance problem of GP classifiers. However, with the evolution of GP, single-objective GP classifiers evaluated by a single fitness function have poor generalization ability. Moreover, using the best evolved GP classifier for decision-making can easily lead to the possibility of misclassification. In this paper, multi-objective GP is used to optimize multiple fitness functions including AUC approximation (Wmw), Distance (Dist), and Complexity to evolve ensemble classifiers, which jointly determines the class labels of unknown instances. Experiments on sixteen datasets show that our multi-objective GP can significantly improve classification performance compared with single-objective GP, and our proposed ensemble classifiers evolved by multi-objective GP can further improve the classification performance than the single best GP classifier. Comparisons with six GP-based and five traditional machine learning algorithms show that our proposed approaches can achieve significantly better classification performance on most cases. •Proposed multi-objective GP can significantly improve the classification performance compared with single-objective GP.•Proposed ensemble classifiers can further improve the classification performance.•Proposed approaches can achieve better performance than six GP-based and five traditional machine learning algorithms.