| | Input: database descriptors, variable importance threshold, accuracy threshold, and number of rounds |
| | Output: selected variables |
| (1) | begin |
| (2) | Create empty optimized model set; |
| (3) | for to Number of rounds do |
| (4) | Define all the descriptor database variables as the current variables; |
| (5) | while True do |
| (6) | Split dataset in training and test partitions; |
| (7) | Create and train the model using training data partition; |
| (8) | Select the most important variables from the trained model; |
| (9) | Calculate the cumulative importance of variables from the trained model; |
| (10) | if max (cumulative importance of variables) < Variable importance threshold then |
| (11) | Exit loop; |
| (12) | end |
| (13) | Train the model using only the most important variables; |
| (14) | Test the trained model and calculate the accuracy; |
| (15) | if Calculated accuracy < Accuracy threshold then |
| (16) | Exit loop; |
| (17) | end |
| (18) | Add current model to optimized model set; |
| (19) | Define the most important variables from the trained model as the current variables; |
| (20) | end |
| (21) | end |
| (22) | Group the models by number of variables; |
| (23) | Remove outliers from the grouped model set; |
| (24) | Select the group of models with the highest frequency and their number of variables “N”; |
| (25) | Rank the variables by the mean of the importance calculated in step 7; |
| (26) | Return the “N” most important variables; |
| (27) | end |
