This paper proposes to view analogical proportion-based classification as a special type of case-based prediction algorithm, in which (i) cases are differences between two instances, and (ii) only maximally similar cases are compared. It then proposes to tweak the CoAT case-based prediction algorithm in order to implement these two key design principles. The resulting analogical proportion-based classifier CoAT-APC shows a performance comparable to state-of-the-art analogical proportion-based classifiers, while implementing a different transfer strategy, based on the minimization of a dataset complexity measure, as opposed to a rule-based approach. Experimental results show the usefulness of combining these two design principles and suggest that the rule-based transfer strategy of analogical proportionbased classifiers has comparatively little impact on the performance of the system.