Class cover catch digraph (CCCD) classifiers are a family of non-parametric prototype selection learners. Previous work has demonstrated that CCCD classifiers perform well in the presence of class imbalance, whereas state-of-the-art classifiers require resampling or ensemble schemes to achieve similar performance. Furthermore, one of the two varieties of CCCD classifier, the random walk (RW-) CCCD classifier, performs better than the pure (P-) CCCD classifier when two classes have some level of support overlap. RW-classifiers suffer from computational complexity and are less accurate when classes are separable, but performs well when overlap occurs. In this work we describe a decision framework that combines P- and RW-CCCD classifiers, achieving superior classification accuracy and sub-cubic computational complexity. We also describe a domain-dependent hybrid classification scheme, which first partitions the domain into class-overlap and well-separated regions, then classifies new examples within each region using RW- and P-CCCD classifiers, respectively.