| 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 |