
Input: the self training set , expected coverage 
Output: the detector set 
: sampling times in nonself space, 
: the number of nonself samples 
: the number of nonself samples covered by detectors 
: the set of candidate detectors 
Step . Initialize the self training set 
Step . Call to generate grid structure which contains selves, where 
is the tree storage of grids and is the line storage of grids; 
Step . Randomly generate a candidate detector . Call
to find the grid 
where is; 
Step . Calculate the Euclidean distance between and all the selves in and its neighbor grids. If 
is identified by a self antigen, abandon it and execute Step ; if not, increase ; 
Step . Calculate the Euclidean distance between and all the detectors in and its neighbor grids. If 
is not identified by any detector, add it into the candidate detector set ; if not, increase , and judge 
whether it reaches the expected coverage , if so, return and the algorithm ends; 
Step . Judge whether reaches sampling times . If , call to implement the screening process of 
candidate detectors, and put candidate detectors which passed this process into , reset ; 
if not, return to Step . 