Advanced Prototype Machines: Exploring Prototypes for Classification

In this paper, we propose advanced prototype machines (APMs). APMs model classes as small sets of highly descriptive prototypes which are well suited for interactive visualization. Thus, APMs offer a method to analyze class models, feature spaces and particular classification scenarios. To derive the prototypes, we introduce ”Push and Grow”, a classification algorithm which is based on a quality measure favoring maximal margins between classes. To explore the derived prototypes, we propose a visualization suite that adapts interactive multi-dimensional scaling to prototype models. The idea of this tool is to display the distance relationships between the prototypes and the objects to be classified. We distinguish three visualization tasks deriving different kinds of information. To shift the visualization error to the less important distance relationships as much as possible, the stress function is adjusted to each of these tasks. APMs achieve fast and accurate classification that is based on compact class models which can be explored by interactive visualization. Our experimental evaluation demonstrates on 14 data sets that APMs achieve better classification accuracy on much less data objects than other kNN-based classifiers. To demonstrate the value of our interactive exploration tool, we provide examples for the derived class models and classification scenarios.