Abstract
Table 10: Dataset Composition
['[BOLD] dataset', '[BOLD] Train', '[BOLD] Test  [BOLD] All', '[BOLD] Test  [BOLD] 2/3 Part', '[BOLD] Test  [BOLD] Cluster']
The following experiments are the part of the first experiment. We do training and testing of the MLP-NN learning model for all those test data compositions. The NN Learning parameters that are used for all experiments are: 500 input nodes, two hidden layers where each layer has 100 nodes with sigmoid activation, and 6 output nodes with softmax function; the learning rate = 1, momentum = 0.5; and epochs = 100. We used mini-batch scenario in the training with the batch size is 100. The learning model performance shows different patterns between MedLine and DrugBank datasets. For both datasets, L1 regularization tends to stabilize in the lower iteration and its training error performance is always less than L0 or L2. The L0 and L2 training error performance pattern, however, shows a slight different behavior between MedLine and DrugBank. For the MedLine dataset, L0 and L2 produce different results for some of iterations. Whereas, the training error performance of L0 and L2 for DrugBank are almost the same in every iteration. Different pattern results are probably due to the variation in the quantity of training data. It can be concluded that, for larger dataset, the contribution of L2 regularization setting is not too significant in achieving better performance. For smaller dataset (MedLine), however, the performance is better even after only few iterations.