Table 1 Hyperparameter search space for convolutional neural networks and support vector machines
From: Machine learning algorithms to identify cluster randomized trials from MEDLINE and EMBASE
Hyperparameter | Values checked | Chosen value |
|---|---|---|
For all models | ||
 Sampling ratio (non-CRT:CRT) | (1411:589), (2411:589), (3411:589), (4411:589) | 3411: 589 |
 Class weights (non-CRT:CRT) | (1:1), (1:5), (0.59:3.4), (1:17), (1:20) | 0.59: 3.4 |
 Metric | AUROC | AUROC |
Convolutional neural network—Word2Vec | ||
 Max length of each abstract | 100, 150, 200, 250, 300, 350 | 300 |
 Batch size (distribution) | Uniform distribution (10, 30) | 11 |
 Learning rate (distribution) | Uniform distribution (0.0005, 0.005) | 0.0047 |
 Dropout rate (distribution) | Uniform distribution (0.1, 0.5) | 0.29 |
 Number of filters (distribution) | Uniform distribution (64, 1526) | 923 |
 Kernel size (distribution) | Uniform distribution (3, 12) | 8 |
 Number of epochs (distribution) | Uniform distribution (3, 20) | 7 |
 Constraint applied to the kernel matrix (distribution) | 1, 1.5, 2, 2.5, 3 | 2 |
 Optimizer (distribution) | Adadelta, Adam | Adam |
 Embedding | Skip-gram; CBOW | Skip-gram |
 Embedding dimensions | 50, 100, 200, 300 | 100 |
 Number of embedding iterations | 5, 10, 15, 20 | 10 |
 Loss | Binary cross-entropy | Binary cross-entropy |
Convolutional neural network—FastText | ||
 Max length of each abstract | 100, 150, 200, 250, 300, 350 | 300 |
 Batch size (distribution) | Uniform distribution (10, 30) | 16 |
 Learning rate (distribution) | Uniform distribution (0.0005, 0.005) | 0.0026 |
 Dropout rate (distribution) | Uniform distribution (0.1, 0.5) | 0.47 |
 Number of filters (distribution) | Uniform distribution (64, 1526) | 532 |
 Kernel size (distribution) | Uniform distribution (3, 12) | 11 |
 Number of epochs (distribution) | Uniform distribution (3, 20) | 14 |
 Constraint applied to the kernel matrix (distribution) | 1, 1.5, 2, 2.5, 3 | 2 |
 Optimizer (distribution) | Adadelta, Adam | Adam |
 Embedding | Skip-gram; CBOW | Skip-gram |
 Embedding dimensions | 50, 100, 200, 300 | 100 |
 Number of embedding iterations | 5, 10, 15, 20 | 10 |
 Loss | Binary cross-entropy | Binary cross-entropy |
Support vector machines | ||
 Kernel | linear, polynomial, sigmoid, or radial basis function | Radial basis function |
 Kernel coefficient | 1, 0.1, 0.01, 0.001, 0.0001 | 0.001 |
 Regularization parameter | 1, 10, 100, 1000 | 100 |
 Ngrams | 1, 1 to 2, 1 to 3, 1 to 4 | 1-gram and bi-gram (1 to 2) |
 Word Vectorization | Bag of Words, TF-IDF | TF-IDF |