A self-tuning toolkit of four GCN input architectures (O-, T-, TP-, TE-GCN) is applied to outcome prediction; the claimed advantage over baselines is unsupported, and the balanced-data perfect scores are likely leakage artifacts.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Data
A self-tuning toolkit of four GCN input architectures (O-, T-, TP-, TE-GCN) is applied to outcome prediction; the claimed advantage over baselines is unsupported, and the balanced-data perfect scores are likely leakage artifacts.