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

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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Bibliographic Details
Main Authors: Ruize Gao, Shaoze Cui, Yu Wang, Wei Xu
Format: Article
Language:English
Published: SpringerOpen 2025-01-01
Series:Financial Innovation
Subjects:
Financial distress prediction
Feature selection
Imbalanced data
Ensemble learning
Particle swarm optimization
Online Access:https://doi.org/10.1186/s40854-024-00745-w
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https://doi.org/10.1186/s40854-024-00745-w

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