AI RESEARCH
CCNETS: A Modular Causal Learning Framework for Pattern Recognition in Imbalanced Datasets
arXiv CS.LG
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ArXi:2401.04139v4 Announce Type: replace Handling class imbalance remains a central challenge in machine learning, particularly in pattern recognition tasks where identifying rare but critical anomalies is of paramount importance. Traditional generative models often decouple data synthesis from classification,leading to a distribution mismatch that limits their practical benefit. To address these shortcomings, we