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Predicting financial distress in high-dimensional imbalanced datasets: a multi-heterogeneous self-paced ensemble learning framework

Abstract Financial distress prediction (FDP) is a critical area of study for researchers, industry stakeholders, and regulatory authorities. However, FDP tasks present several challenges, including high-dimensional datasets, class imbalances, and the complexity of parameter optimization. These issue...

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Uloženo v:
Hlavní autoři: Ruize Gao, Shaoze Cui, Yu Wang, Wei Xu
Médium: Artigo
Jazyk:Inglês
Vydáno: SpringerOpen 2025-01-01
Edice:Financial Innovation
Témata:
Financial distress prediction
Feature selection
Imbalanced data
Ensemble learning
Particle swarm optimization
On-line přístup:https://doi.org/10.1186/s40854-024-00745-w
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