Ensemble classifiers using multi-objective Genetic Programming for unbalanced data
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{MENG:2024:asoc,
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author = "Wenyang Meng and Ying Li and Xiaoying Gao and
Jianbin Ma",
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title = "Ensemble classifiers using multi-objective Genetic
Programming for unbalanced data",
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journal = "Applied Soft Computing",
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volume = "158",
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pages = "111554",
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year = "2024",
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ISSN = "1568-4946",
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DOI = "doi:10.1016/j.asoc.2024.111554",
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URL = "https://www.sciencedirect.com/science/article/pii/S1568494624003284",
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keywords = "genetic algorithms, genetic programming,
Multi-objective, Ensemble, Unbalanced",
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abstract = "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",
- }
Genetic Programming entries for
Wenyang Meng
Ying Li
Xiaoying (Sharon) Gao
Jianbin Ma
Citations