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thesis/citations thesis/citations

• Bank Marketing Dataset — Model Architecture
  • Justification
  • Sources

Sources used to justify implementation choices in the thesis. Linked from thesis.

Bank Marketing Dataset — Model Architecture

Justification

The standard neural network architecture for the UCI Bank Marketing dataset in the literature is a 2-hidden-layer MLP with 128 units per layer and ReLU activations. This is used in our experiment-bank-marketing.py as: Linear(input, 128) → ReLU → Dropout(0.3) → Linear(128, 128) → ReLU → Dropout(0.3) → Linear(128, 1)

Sources

• ktamburi/Bank-Marketing-UCI-Dataset-Neural-Network (GitHub) Most-cited student/practitioner reference for NN on this dataset. Uses exactly 128→128 architecture with ReLU, final softmax (we use sigmoid for binary). Justifies our hidden layer size choice.
• A Closer Look at Deep Learning Methods on Tabular Datasets (arXiv 2407.00956) Benchmarks MLP and TabNet on tabular datasets including bank marketing. Confirms that simple MLPs with 2-3 layers are the standard DL baseline for this domain.
• Risk-Sensitive ML for Financial Decision Modeling Under Imbalanced Data (Entropy 2025) Directly addresses class imbalance on the bank marketing dataset. Supports our use of pos_weight and AUC as the primary metric given IR ~8:1.
• Bank Marketing Data — Imbalanced Dataset (Medium) Confirms the dataset has ~11.3% positive rate (IR ~8:1), motivating use of imbalance-aware losses (GCE, MAE, focal, asymmetric, ROLL).