Circles: Inter-Model Comparison of Multi-Classification Problems with High Number of Classes
This addresses the challenge of inter-model comparison for researchers and practitioners working with multi-class classification problems involving hundreds or thousands of classes, though it appears incremental as it builds on existing visualization techniques.
The authors tackled the problem of visualizing and comparing multiple classification models with a high number of classes, presenting an interactive tool called Circles that enables visual inter-model comparison of up to 9 models with 1,000 classes in one view using a radial line layout to reduce clutter.
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