Hierarchy Extraction

Knowledge Discovery: Self-Organized Rule Extraction
An add-on for the Classer toolkit implements the ARTMAP Information Fusion System, the basis for the a self-organized approach for extracting rules describing relationships between class labels that describe a domain of interest. More basically, the cartoon shown below illustrates the issue; in the real world, we constantly have to deal with multiple labels for objects. Rather than get confused by the apparent contradictions, the ARTMAP Information Fusion System uses these multiple labels to infer relationships. This approach is best described by the abstract for Carpenter, Martens & Ogas (2005):

Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when evidence variously suggests that the class of an object is car, truck, or airplane. The methods described here address a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that the class of an object is car, vehicle, and man-made. Underlying relationships among classes are assumed to be unknown to the automated system or the human user. The ARTMAP information fusion system uses distributed code representations that exploit the capacity of the neural network for one-to-many learning in order to produce self-organizing expert systems that discover hierarchical knowledge structures. The fusion system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples, but is not limited to the image domain.