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.
DL-Learner is licenced under the terms of the GNU General Public License.
Copyright © 2007-2019 Jens Lehmann