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Developing distance-based genetic programming classifiers by reconstructing datasets for imbalanced binary classification
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DOI：10.1016/j.patcog.2025.112825.png)
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Abstract
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• Propose a dataset reconstruction strategy that generates equal numbers of instance tuples from majority and minority classes. • Propose a distance-based GP classifier construction method and this method can effectively deal with imbalanced binary datasets. • Our proposed method achieves significantly better classification performance than eight GP-based classification methods, and achieves competitive results with six traditional machine learning algorithms.
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