Table 6 Summary of baseline configurations used in this paper.
Baseline | Implementation (library/module) | Key architecture hyperparameters | Training hyperparameters |
|---|---|---|---|
GCN | PyTorch Geometric GCNConv | hidden dim = 256; #GCN layers = 3; ReLU after each layer; linear classifier Linear (256 → C) | Adam lr = 0.001; epochs = 1000; CE loss |
GAT | PyTorch Geometric GATConv | hidden dim = 256; heads = 4; #GAT layers = 3; (intermediate dim = 256×heads); ReLU; linear classifier Linear (256×heads → C) | Adam lr = 0.001; epochs = 1000; CE loss |
ViT | PyTorch TransformerEncoder/Layer | token proj: Linear (D → 256); learnable pos embed (1 × 256); encoder layers = 3; heads = 4; FFN dim = 4 × 256; mean pooling; classifier Linear (256 → C) | Adam lr = 0.001; epochs = 1000; CE loss |
Hybrid | PyTorch (Conv1d + Transformer) | 1D-CNN: Conv1d (1→64, k = 3) + MP (2) + Conv1d (64→128, k = 3) + MP (2); Transformer dim = 128, heads = 2, layers = 3; classifier Linear (128 → C) | Adam lr = 0.001; epochs = 1000; CE loss |
EfficientFormer | PyTorch (Conv1d embed + Transformer) | patch embed: Conv1d (1→64, k = 3) + GELU + Conv1d (64→128, k = 3, stride = 2) + GroupNorm; Transformer dim = 128, heads = 4, layers = 4, FFN dim = 4 × 128, GELU; mean pooling; classifier Linear (128 → C) | Adam lr = 0.001; epochs = 800; CE loss |
