| Input: Training set |
| Output: Hyper-edge set |
| Step 1. (Initialize hypergraph) |
| FOR each in X DO |
| Create one hyper-edge of sample : First, Inherit attributes from the sample randomly |
| and replace the values randomly on the rest attribute. Second, inherits the decision |
| attribute of . Third, if has missing value, we fill the attribute value in terms with |
| continuous attributes or discrete attribute. |
| ; |
| END FOR |
| Step 2. (Classify the training set) |
| According to formula (7), calculating the neighborhood threshold for each sample; |
| FOR each in X DO |
| FOR each in E DO |
| According to formula (1), calculate the relevant degree of and , . |
| IF THEN ; END IF |
| END FOR |
| FOR each in DO |
| IF THEN |
| ; // is the decision attribute value |
| END IF |
| END FOR |
| Compute the classification of , . |
| END FOR |
| Compute the correctly classified ratio of the training set: accuracy; |
| IF or THEN GOTO Step ; |
| ELSE GOTO Step ; |
| END IF |
| Step 3. (Replace hyper-edge) |
| ; //the initialize of hyper-edge replacement set |
| FOR each in DO |
| According to Definition 7 and formula (5), calculate the confidence degree of : . |
| IF THEN ; ; END IF |
| END FOR |
| While we replace the hyper-edge, it is prior to replace the hyper-edge which is generated by |
| the sample with missing values. |
| WHILE () |
| Generate a new hyper-edge through the process similar to Step . |
| ; ; |
| END WHILE |
| GOTO Step ; |
| Step 4. (Return) |
| RETURN E; |
