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Evolving ensembles using multi-objective genetic programming for imbalanced classification
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DOI：10.1016/j.knosys.2022.109611.png)
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Multi-objective Genetic Programming (MGP) plays a prominent role in generating Pareto optimal classifier sets and making trade-offs among multiple classes adaptively. However, the existing MGP algorithms show poor performance and are difficult to implement when dealing with imbalanced classification problems. This work proposes a new MGP-based algorithm designed for imbalanced classification. Firstly, an efficient evolutionary strategy with nondominated sorting, environmental selection, and an archiving mechanism is designed to optimize the false positive rate, the false negative rate and reduce the size of the resulting tree. Then, a weighted ensemble decision is made according to each classifier's performance in the majority and minority classes to obtain final classification results. Experimental results on 21 binary-class datasets and 17 multi-class datasets show that the proposed method outperforms existing ones in several commonly used imbalanced classification metrics.(c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Imbalanced classification
Multi -objective genetic programming
Evolutionary algorithm
Ensemble decision
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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