| Input: |
| : a stream of examples |
| : a set of symbolic attributes |
| : heuristic evaluation function for node splitting |
| : one minus desired probability of choosing the correct attribute at any given node. |
| : number of samples between estimation of growth |
| : sorted list of Hoeffding bound values |
| : total number of values in |
| : new Hoeffding Bound value seen at the node |
| : adaptive threshold |
| : subset of |
| : 5% of examples in . Threshold for checking the eligibility of a node to be part of HT |
| Size of |
| Output: |
| A decision tree HT |
| Procedure EnhancedVFDT(, , , , ) |
| BEGIN: |
| A stream of examples arrives |
| IF (), THEN TreeInitialization(S, X) |
| Get an Initialized HT with a single root node |
| IF (), THEN NewStreamSample(S, X) |
| Label with the majority class among the samples seen so far at |
| Let be the number of samples seen at |
| IF the samples seen so far at are not all of the same class and |
| THEN |
| Compute for each attribute using |
| PrunedMean = AccuracyEVFDT(, , , ) |
| Let be the attribute with highest and be the attribute with second-highest |
| Compute using (1) |
| Let |
| IF ( or ≤ PrunedMean) and , THEN split as a branch |
| FOR each branch of split |
| Add a new leaf and let |
| Let be the obtained by predicting the most frequent class at |
| FOR each class and each value of each attribute |
| Let . |
| END-FOR |
| END-FOR |
| END-IF |
| ELSE Pruning(, , , , HT) |
| Return HT |
| END: |
