Group-based meta-classification
Virtually all existing classification techniques label one sample at a time. In this paper, we highlight the potential benefits of group based classification (GBC), where the classifier labels a group of homogeneous samples. In this way, GBC can take advantage of the additional prior knowledge that all samples belong to the same, unknown, class. We pose GBC in a generic hypothesis testing framework requiring the selection of an appropriate sample and test statistic. We then evaluate one simple example of GBC on both synthetic and real data sets and demonstrate that GBC may be a promising approach in applications where the test data can be arranged into homogenous subsets.