Algorithmic and mathematical modeling for synthetically controlled overlapping

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 4, 2025

For most classifiers, overlapping regions, where various classes are difficult to distinguish, affect the classifier's overall performance in multi-class imbalanced data more than imbalance itself. In problem-data space, overlapped samples share similar characteristics, resulting a complex boundary, making it separate of from each other, causing degradation. The research community agreed upon relationship class issues with classifier performance, but how much is affected still unanswered. There also gap literature demonstrate different levels problems. Accordingly, this paper, four algorithms implemented synthetically generate controlled be used multiclass datasets using schemes show worst effect overlapping. Experiments involve state-of-the-art non-parametric support vector machines, k-nearest neighbor, and random forest, classify these validate on their learning. models test suitability, stability, versatility proposed for highlight growing problems having an distribution issues. experimental results 20 real-world datasets, level underlying classifiers.

Language: Английский

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Adaptive synthetic oversampling with Poincaré distance and Two-stage weight assignment

Neurocomputing, Journal Year: 2025, Volume and Issue: unknown, P. 130436 - 130436

Published: May 1, 2025

Language: Английский

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PRO-SMOTEBoost: An Adaptive SMOTEBoost Probabilistic Algorithm for Rebalancing and Improving Imbalanced Data Classification

Information Sciences, Journal Year: 2024, Volume and Issue: unknown, P. 121548 - 121548

Published: Oct. 1, 2024

Language: Английский

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