Interactive visualization and analysis of the class boundaries is important because it tells us how and why the classes differ. However, the problem of modeling the boundary of classes of arbitrary size, shape and density is challenging. The boundary of a class should not be limited to the points/shape which encloses the points within the class but it should be, the points/shape which encloses the region of influence of a class. The "region of influence" refers to the space around the class where any point lying within the region is likely to be classified to the class based on a nearest neighbor classifier. We have developed interactive boundary visualization toolkit for classified datasets which provides insights about the classifier model used on the dataset. Our algorithm first generates a candidate boundary set for each class based on reverse k-nearest neighbors approach and extends this boundary iteratively through the region of influence of the class. Further, we present these boundary points enclosing the region of influence as a linear approximated shape using triangulation techniques. We show experimental results on 2D and 3D datasets.
Pallav Tinna, Kamalakar Karlapalem, "Exploiting Regions of Influence to Visualize Class Boundaries" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Visualization and Data Analysis, 2016, https://doi.org/10.2352/ISSN.2470-1173.2016.1.VDA-500