Circles: Inter-Model Comparison of Multi-Classification Problems with
  High Number of Classes
Nina Mir Ragaad AlTarawneh Shah Rukh Humayoun
The recent advancements in machine learning have motivated researchers to
generate classification models dealing with hundreds of classes such as in the
case of image datasets. However, visualization of classification models with
high number of classes and inter-model comparison in such classification
problems are two areas that have not received much attention in the literature,
despite the ever-increasing use of classification models to address problems
with very large class categories. In this paper, we present our interactive
visual analytics tool, called Circles, that allows a visual inter-model
comparison of numerous classification models with 1K classes in one view. To
mitigate the tricky issue of visual clutter, we chose concentric a radial line
layout for our inter-model comparison task. Our prototype shows the results of
9 models with 1K classes