Marker interface for heuristics in the refinement operator based learning approach.
Represents a node in the search tree.
This heuristic compares two nodes by computing a score using the number of covered negatives and the horizontal expansion factor of a node as input.
This heuristic combines the following criteria to assign a double score value to a node: quality/accuracy of a concept (based on the full training set, not the negative example coverage as the flexible heuristic) horizontal expansion accuracy gain: The heuristic takes into account the accuracy difference between a node and its parent.
This comparator is stable, because it only takes covered examples, concept length and the concepts itself (using again a stable comparator) into account, which do not change during the run of the algorithm.
The DL-Learner learning algorithm component for the example based refinement operator approach.
New experimental refinement operator approach, which takes obtained information about concrete examples in an algorithm run stronger into account.
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