| Input: the training sample set: , the radius of neighborhood: . |
| Output: hyper-edge set: . |
| Step 1. (Initialization) |
| ; //Initialize the hyper-edge set. |
| According to the formula (2), (14), calculate the radius of each sample. |
| FOR each in X DO |
| ; //Count the number of hyper-edges generated by each sample. |
| WHILE () //Each of samples generates five hyper-edges. |
| Generate hyper-edge according to and seven tenth attributions of inherit attribution |
| values of ; |
| Calculate the distance between and each sample according to Formula (2) respectively; |
| Calculate and respectively, according to Definitions 10 and Definition 11; |
| IF THEN ; |
| ELSE ; // is a random number in . |
| Calculate and respectively, according to Definition 10; |
| IF THEN ; |
| ELSE ; |
| END IF |
| END IF |
| ++; |
| END WHILE |
| END FOR |
| Step 2. (Training Set Classification) |
| Calculate of hyper-edge set according to Definition 7 and Formula (6); |
| FOR each in DO |
| Calculate and according to Definition 12; |
| IF THEN ; |
| ELSE ; |
| END IF |
| END FOR |
| Calculate the classification accuracy of training data set: Train-accuracy. |
| IF Train-accuracy > 0.95 THEN GOTO Step 4; |
| ELSE GOTO Step 3; |
| END IF |
| Step 3. (Hyper-edge Replacement) |
| ; //Number of hyper-edges that should be replaced. |
| FOR each in DO |
| Calculate the confidence-degree of according to Definition 8 and Formula (8); |
| IF THEN ++; |
| END IF |
| END FOR |
| WHILE () |
| Generate a new hyper-edge according to Step 1; |
| −−; |
| END WHILE |
| GOTO Step 2; |
| Step 4. (Return Hypergraph) |
| RETURN . |
