  The case difference heuristic (CDH) approach is a knowledge-light method for
learning case adaptation knowledge from the case base of a case-based reasoning
system. Given a pair of cases, the CDH approach attributes the difference in
their solutions to the difference in the problems they solve, and generates
adaptation rules to adjust solutions accordingly when a retrieved case and new
query have similar problem differences. As an alternative to learning
adaptation rules, several researchers have applied neural networks to learn to
predict solution differences from problem differences. Previous work on such
approaches has assumed that the feature set describing problems is predefined.
This paper investigates a two-phase process combining deep learning for feature
extraction and neural network based adaptation learning from extracted
features. Its performance is demonstrated in a regression task on an image
data: predicting age given the image of a face. Results show that the combined
process can successfully learn adaptation knowledge applicable to nonsymbolic
differences in cases. The CBR system achieves slightly lower performance
overall than a baseline deep network regressor, but better performance than the
baseline on novel queries.
