ProSeCo: Visual analysis of class separation measures and dataset characteristics. (May 2021) - Bernard, Jürgen; Hutter, Marco; Zeppelzauer, Matthias; Sedlmair, Michael; Munzner, Tamara
Highlights: Design and implementation of the interactive analysis tool ProSeCo. Tackles the challenge of comparing separation measures qualitatively and quantitatively. Comparison between up to 20 class separation measures. Comparison between up to 4 dimensionality reduction techniques. Analysis of 7 data characteristics in the context of class separation measures. Graphical abstract: Abstract: Class separation is an important concept in machine learning and visual analytics. We address the visual analysis of class separation measures for both high-dimensional data and its corresponding projections into 2D through dimensionality reduction (DR) methods. Although a plethora of separation measures have been proposed, it is difficult to compare class separation between multiple datasets with different characteristics, multiple separation measures, and multiple DR methods. We present ProSeCo, an interactive visualization approach to support comparison between up to 20 class separation measures and up to 4 DR methods, with respect to any of 7 dataset characteristics: dataset size, dataset dimensions, class counts, class size variability, class size skewness, outlieriness, and real-world vs. synthetically generated data. ProSeCo supports (1) comparing across measures, (2) comparing high-dimensional to dimensionally-reduced 2D data across measures, (3) comparing between different DR methods across measures, (4) partitioning with respect to a dataset characteristic, (5) comparing partitions for a selected characteristic across measures, and (6) inspecting individual datasets in detail. We demonstrate the utility of ProSeCo in two usage scenarios, using datasets [1] posted at https://osf.io/epcf9/ .