| Prerequisite: |
| Obtain healthy (negative) and unhealthy (positive) observations |
| Split the obtained data into two groups; training and validation |
| Initialization, let: |
| Training mode = True |
| Threshold |
| Selected features = All features |
| threshold_optimization_indicator = False |
| MMTS Algorithm |
| (1) IF Training mode == True |
| (2) While threshold_optimization_indicator = False Do |
| (3) , (i.e. by using the correlation matrix of the negative observations, and ) |
| (4) , (i.e. use Taguchi approach for features selection and update ) |
| (5) , (i.e. recalculate Mahalanobis distance using the new features ) |
| (6) Classify observations based on the threshold , and the selected features |
| (7) IF |
| (8) Observation is classified as negative |
| (9) Else |
| (10) Observation is classified as positive |
| (11) End |
| (12) Calculate the True Positive rate and the False Positive rate |
| (13) , (i.e. calculate the fitness function) |
| (14) IF the threshold optimization termination criteria is reached |
| (15) threshold_optimization_indicator = True |
| (16) Select threshold , and features, that will result in minimum fitness function |
| (17) Else |
| (18) Use genetic algorithm to find the threshold that will minimize the fitness function |
| (19)End |
| (20)End While threshold |
| (21) Training mode = False, the optimum threshold , and the optimum features |
| (22)Else |
| (23) Using the threshold , and features , calculate the Mahalanobis distance, |
| (24)IF |
| (25) Observation is classified as negative |
| (26)Else |
| (27) Observation is classified as positive |
| (28)End |
| (29)End IF |
