- Antony Pearson
- Auburn University
- Date: April 26, Tuesday, 2022
- Time: 12:00 -- 12:50
- Abstract: Class cover catch digraph (CCCD) classifiers are a family of nonparametric prototype selection learners. Previous work has demonstrated that CCCD classifiers perform well in the context of class imbalance, whereas state-of-the-art classifiers require resampling or ensemble schemes to achieve similar performance. It is also known that one of the two well-known types of CCCD classifier, the random walk (RW-), performs better than the pure (P-) CCCD classifier in the context of class overlap, i.e., when two classes have substantial similarity. Unfortunately, RW-classifiers suffer from large training time and are less accurate when there is no class overlap. In this work we describe an adaptive decision framework for pure versus random walk classifiers, which may offer superior classification accuracy and sub-cubic computational complexity. We propose a hybrid classifier borrowing the strengths of both types of CCCD classifier that partitions the sample space into a region of high class overlap where a RW-CCCD is trained, and a region in which class supports are separated, where a P-CCCD is trained. The hybrid strategy offers superior classification accuracy compared P-CCCD or RW-CCCD classifiers trained individually, and improved computational complexity over RW-CCCD classifiers.