| | Input D = {(x1, y1), (x2, y2), …, (xN, yN),} denote the training data with xi = (xi,1, xi,2, …, xi,p) |
| (1) | Output classified test data |
| (2) | Assumption |
| (3) | LA : Learning automata |
| (4) | DTr = {DT1, DT2, … , DTR} denote the base learners |
| (5) | αi: LA action//Choose DTr |
| (6) | a: Reward parameter |
| (7) | b: Penalty parameter |
| (8) | Pool : All the trained base learners |
| (9) | Algorithm |
| (10) | For r = 1 to R do |
| (11) | Create a dataset Dt, by sampling (N/R) items, randomly with replacement from D |
| (12) | Train DTr using Dt, and add to the pool |
| (13) | end//for |
| (14) | For each test sample |
| (15) | { |
| (16) | LA = new LA//Create an LA object from LA class |
| (17) | While ((LA convergences to an action) or (LA exceeds predefined iteration number)) |
| (18) | { |
| (19) | Select one of the actions at random and execute it, by the LA, Let it be αi |
| (20) | If (αi predicts the new test sample correctly) then//Update the probability of selection vector |
| (21) | //reward the selected αi |
| (22) | else |
| (23) | //Penalty the selected αi |
| (24) | }//end while |
| (25) | }//end for |
| (26) | Return DTr |
| (27) | Classified test data = the prediction of DTr |
| (28) | End.//algorithm |
