ClassSPLOM -- A Scatterplot Matrix to Visualize Separation of Multiclass Multidimensional Data

TL;DR

ClassSPLOM is a visualization tool that uses scatterplot matrices and ROC curves to help interpret multiclass classification results on multidimensional data, demonstrated on Arabic dialects.

Contribution

It introduces a novel visualization method combining LDA projections and ROC analysis to better understand class separation in high-dimensional data.

Findings

Provides perceptual insights into class separation

Enhances interpretation of classification confusion

Demonstrates effectiveness on Arabic dialects data

Abstract

In multiclass classification of multidimensional data, the user wants to build a model of the classes to predict the label of unseen data. The model is trained on the data and tested on unseen data with known labels to evaluate its quality. The results are visualized as a confusion matrix which shows how many data labels have been predicted correctly or confused with other classes. The multidimensional nature of the data prevents the direct visualization of the classes so we design ClassSPLOM to give more perceptual insights about the classification results. It uses the Scatterplot Matrix (SPLOM) metaphor to visualize a Linear Discriminant Analysis projection of the data for each pair of classes and a set of Receiving Operating Curves to evaluate their trustworthiness. We illustrate ClassSPLOM on a use case in Arabic dialects identification.