ClassSPLOM -- A Scatterplot Matrix to Visualize Separation of Multiclass Multidimensional Data
This addresses the problem of interpreting classification results for users in fields like dialect identification, but it is incremental as it builds on existing visualization metaphors.
The paper tackles the challenge of visualizing multiclass classification results for multidimensional data by introducing ClassSPLOM, which uses a Scatterplot Matrix with Linear Discriminant Analysis projections and ROC curves to provide perceptual insights, as demonstrated in an Arabic dialects identification case.
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.