On ten KEEL datasets, KANs outperform MLPs on raw imbalanced data but resampling and focal loss degrade KANs while MLPs with those techniques match KAN performance at far lower cost.
Enhancing Synthetic Oversampling for Imbalanced Datasets Using Proxima-Orion Neighbors and q-Gaussian Weighting Technique
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abstract
In this article, we propose a novel oversampling algorithm to increase the number of instances of minority class in an imbalanced dataset. We select two instances, Proxima and Orion, from the set of all minority class instances, based on a combination of relative distance weights and density estimation of majority class instances. Furthermore, the q-Gaussian distribution is used as a weighting mechanism to produce new synthetic instances to improve the representation and diversity. We conduct a comprehensive experiment on 42 datasets extracted from KEEL software and eight datasets from the UCI ML repository to evaluate the usefulness of the proposed (PO-QG) algorithm. Wilcoxon signed-rank test is used to compare the proposed algorithm with five other existing algorithms. The test results show that the proposed technique improves the overall classification performance. We also demonstrate the PO-QG algorithm to a dataset of Indian patients with sarcopenia.
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Kolmogorov Arnold Networks (KANs) for Imbalanced Data -- An Empirical Perspective
On ten KEEL datasets, KANs outperform MLPs on raw imbalanced data but resampling and focal loss degrade KANs while MLPs with those techniques match KAN performance at far lower cost.