Circles: Inter-Model Comparison of Multi-Classification Problems with High Number of Classes

TL;DR

This paper introduces Circles, an interactive visualization tool designed for comparing multiple classification models with thousands of classes, addressing visualization challenges and enabling effective inter-model analysis.

Contribution

The paper presents a novel radial layout visualization tool for inter-model comparison of high-class-count classifiers, filling a gap in visualization techniques for large-scale classification.

Findings

Effective visualization of 9 models with 1,000 classes each.

Radial layout reduces visual clutter in high-class comparisons.

Supports interactive analysis of multi-model classification results.

Abstract

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