Research Article
Semi-Supervised Predictive Clustering Trees for (Hierarchical) Multi-Label Classification
Table 1
The proposed algorithm for learning of semi-supervised predictive clustering trees.
| Procedure SSL-PCT | Input: A dataset , a parameter | Output: A predictive clustering tree | (1): | (2): if then | (3): for eachdo | (4): = SSL-PCT(, ) | (5): return | (6): else | (7): return |
| Procedure OptimizeParamW | Input: A dataset ; a set of values , ; a number of folds k | Output: A value | (1): for eachdo | (2): | (3): return |
| Procedure | Input: A dataset , a parameter | Output: The best test (), its heuristic score (), and the partition () it induces on the dataset (E) | (1): | (2): for each possible test do | (3): partition induced by on | (4): | (5): ifthen | (6): | (7): return |
| Procedure CrossValidate | Input: A dataset , , a number of folds k | Output: An accuracy measure | (1): = partition into k folds | (2): for eachdo | (3): = SSL-PCT | (4): = evaluate | (5): return |
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