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.
@article{arxiv.2309.05676,
title = {MultiCaM-Vis: Visual Exploration of Multi-Classification Model with High Number of Classes},
author = {Syed Ahsan Ali Dilawer and Shah Rukh Humayoun},
journal= {arXiv preprint arXiv:2309.05676},
year = {2023}
}