MultiCaM-Vis: Visual Exploration of Multi-Classification Model with High
  Number of Classes
Syed Ahsan Ali Dilawer Shah Rukh Humayoun
Visual exploration of multi-classification models with large number of
classes would help machine learning experts in identifying the root cause of a
problem that occurs during learning phase such as miss-classification of
instances. Most of the previous visual analytics solutions targeted only a few
classes. In this paper, we present our interactive visual analytics tool,
called MultiCaM-Vis, that provides \Emph{overview+detail} style parallel
coordinate views and a Chord diagram for exploration and inspection of
class-level miss-classification of instances. We also present results of a
preliminary user study with 12 participants.