Input 
(i) : a training set, where is an matrix containing the input values and is an 
Ndimensional column vector containing the class labels. 
(ii) : number of attribute subsets (or : number of input attributes contained in each subset). 
(iii) : a base learning. 
(iv) : number of iterations for Rotation Forest. 
(v) : number of iterations for AdaBoost. 
(vi) : a data point to be classified. 
Training Phase 
For 
(1) use the steps similar to those in Rotation Forest to compute the Rotation matrix, say, and let be the 
training set for classifier . 
(2) Initialize the weight distribution over as . 
(3) For 
(a) According to distribution perform N extractions randomly from with replacement to compose a new set . 
(b) Apply to to train a classifier and then compute the error of as 

(c) If then set . and go to step (a); if , then set to continue 
the following iterations. 
(d) Choose 
(e) Update the distribution over as: 

where is a normalization factor being chosen so that is a probability distribution over . 
Endfor 
(4) Let 
Endfor 
Output 
(i) The class label for x predicted by the final ensemble as 
, 
where I(·) is an indicator function. 