MultiCaM-Vis: Visual Exploration of Multi-Classification Model with High Number of Classes

Syed Ahsan Ali Dilawer, Shah Rukh Humayoun
9/9/2023
cs.HCcs.LG

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Abstract

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.

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Cite this paper

@article{dilawer2023multicamvis,
  title  = {MultiCaM-Vis: Visual Exploration of Multi-Classification Model with High Number of Classes},
  author = {Syed Ahsan Ali Dilawer and Shah Rukh Humayoun},
  year   = {2023},
  eprint = {2309.05676},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.05676v1}
}

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