MultiCaM-Vis: Visual Exploration of Multi-Classification Model with High Number of Classes
This addresses a gap for machine learning experts in exploring models with high class counts, though it appears incremental as it builds on existing visual analytics approaches.
The paper tackles the problem of visualizing multi-classification models with many classes to help experts identify root causes like misclassification, presenting MultiCaM-Vis, an interactive tool with parallel coordinate views and a Chord diagram, and reports results from a preliminary user study with 12 participants.
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.