A Pattern-Hierarchy Classifier for Reduced Teaching
  This paper describes a design that can be used for Explainable AI. The lower
level is a nested ensemble of patterns created by self-organisation. The upper
level is a hierarchical tree, where nodes are linked through individual
concepts, so there is a transition from mixed ensemble masses to specific
categories. Lower-level pattern ensembles are learned in an unsupervsised
manner and then split into branches when it is clear that the category has
changed. Links between the two levels define that the concepts are learned and
missing links define that they are guessed only. This paper proposes some new
clustering algorithms for producing the pattern ensembles, that are themselves
an ensemble which converges through aggregations. Multiple solutions are also
combined, to make the final result more robust. One measure of success is how
coherent these ensembles are, which means that every data row in the cluster
belongs to the same category. The total number of clusters is also important
and the teaching phase can correct the ensemble estimates with respect to both
of these. A teaching phase would then help the classifier to learn the true
category for each input row. During this phase, any classifier can learn or
infer correct classifications from some other classifier's knowledge, thereby
reducing the required number of presentations. As the information is added,
cross-referencing between the two structures allows it to be used more widely,
where a unique structure can build up that would not be possible by either
method separately.
